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The Global Machine Learning in Logistics Market size is expected to be worth around USD 47.6 million by 2035, from USD 5.1 million in 2025, growing at a CAGR of 24.9% during the forecast period from 2026 to 2035. North America held a dominant market position, capturing more than 44.8% share and generating USD 2.2 million in revenue.
Machine Learning in Logistics refers to the use of intelligent algorithms to improve transport, warehousing, inventory, and delivery operations. It helps logistics teams study large data sets, predict demand, plan better routes, reduce delays, and improve asset use. These systems support faster decisions and more reliable supply chain performance.
nal clouds and on-premise systems, Intel underpins the infrastructure layer of AI-driven logistics in the U.S.
In 2025, the Software segment held a dominant market position, capturing a 68.5% share of the Global Machine Learning in Logistics Market. This dominance is due to the rising need for intelligent logistics software that can support planning, routing, warehouse visibility, and maintenance decisions. Logistics firms are using software platforms to study large data sets, improve shipment control, and make faster decisions across transport, inventory, and order management activities.
Software also allows companies to connect machine learning models with existing supply chain systems. This makes it easier to manage demand changes, delivery delays, and asset performance from one digital layer. As logistics networks become more complex, software remains central to improving accuracy, speed, and operating discipline.
For instance, in March 2026, IBM introduced a predictive logistics platform that uses machine learning to improve routing, monitor shipments in real time, and flag likely disruptions before they occur. This kind of software shows how machine learning is being embedded directly into logistics workflows, pushing more companies to upgrade from manual tools to intelligent applications.
In 2025, the Cloud-Based segment held a dominant market position, capturing a 58.7% share of the Global Machine Learning in Logistics Market. This dominance is due to the growing preference for flexible logistics systems that can be accessed across warehouses, transport teams, and regional offices. Cloud-based deployment helps companies share real-time information, manage distributed operations, and support faster planning without depending heavily on local infrastructure.
Cloud platforms also support easier updates, better data storage, and smoother integration with other supply chain tools. Logistics firms prefer this model because it can scale with order volumes and new routes. It also helps decision makers track operations more clearly across multiple locations and partners.
For instance, in January 2025, Oracle announced new AI-enabled logistics and order management features within its cloud supply chain suite. Delivered as SaaS, these capabilities let shippers improve routing decisions, predict transit issues, and manage global trade from a single cloud environment, reinforcing why many logistics teams prefer cloud deployment for flexibility and faster updates.
In 2025, the Large Enterprises segment held a dominant market position, capturing a 62.8% share of the Global Machine Learning in Logistics Market. This dominance is due to the wide logistics networks and complex supply chain needs of large enterprises. These companies handle higher shipment volumes, multiple warehouses, and broad customer bases, which makes machine learning more useful for routing, demand planning, risk tracking, and inventory coordination.
Large enterprises also have stronger budgets and internal teams to adopt advanced logistics systems. They can invest in machine learning tools to improve asset use, reduce delays, and manage large data flows. Even small improvements in planning can create meaningful operational gains across their wider networks.
For instance, in October 2025, SAP detailed new AI-powered supply chain orchestration tools aimed at large, networked enterprises. These solutions connect planning, logistics, and procurement into one intelligent layer, enabling multinational companies to detect risks, coordinate responses, and standardize decisions across regions, which is driving machine learning adoption among big organizations first.
In 2025, the Demand Forecasting and Planning segment held a dominant market position, capturing a 38.5% share of the Global Machine Learning in Logistics Market. This dominance is due to the strong need for accurate demand visibility in modern logistics. Companies use machine learning to study order patterns, seasonal changes, customer behavior, and stock movement. This helps planners make better decisions on inventory, transport capacity, and warehouse preparation.
Demand forecasting and planning also help reduce stock shortages and excess inventory. Logistics firms can respond faster when demand shifts across regions or product categories. Better planning supports smoother deliveries, lower waste, and improved coordination between suppliers, warehouses, carriers, and retail channels.
For instance, in June 2025, AWS Supply Chain added new planning features that use machine learning to anticipate demand patterns and support upstream supply decisions. These enhancements help logistics and supply chain teams react earlier to demand shifts, align capacity, and reduce uncertainty, which is exactly why demand forecasting and planning are leading use cases.
In 2025, the Retail and E-commerce segment held a dominant market position, capturing a 42.5% share of the Global Machine Learning in Logistics Market. This dominance is due to the fast movement of online orders, frequent delivery changes, and high customer expectations in retail and e-commerce. These businesses need machine learning to improve order routing, delivery planning, inventory placement, and return handling across large and active networks.
Retail and e-commerce firms also depend on strong logistics visibility to manage short delivery windows and changing demand patterns. Machine learning helps them forecast orders, reduce delays, and improve warehouse flow. This supports better customer service while helping companies control costs in highly active fulfillment environments.
For instance, in October 2025, NVIDIA showcased digital twin technology that lets retailers simulate warehouse operations and last-mile delivery using AI. By testing different layouts and routing strategies virtually, retail and e-commerce players can improve fulfillment speed and reliability, which strengthens the business case for machine learning in their logistics networks.
Data and Integration Gaps
Data and integration gaps remain a major restraint for the market. Many logistics firms still use separate systems for transport, warehouse, inventory, and customer orders. When these systems do not connect properly, machine learning models may receive incomplete or inconsistent data.
This makes implementation slower and less effective, especially for firms with older technology setups. Poor data quality can reduce trust in model outputs and delay adoption. Companies need better data cleaning, system integration, and process alignment before machine learning can deliver strong operational value.
For instance, in October 2025, IBM’s partnership with S&P Global to embed AI agents in supply chain tools highlighted how data silos across procurement, trade, and risk sources complicate analytics. The collaboration aims to knit these datasets together, showing that even large players must invest heavily to overcome fragmented information before AI can fully support event logistics decisions.
Predictive and automated operations offer strong opportunities for machine learning in logistics. These systems can help firms forecast demand, identify equipment issues early, improve warehouse planning, and support smarter dispatch decisions. This allows logistics teams to act before problems affect delivery performance.
Automation also supports better coordination across transport, storage, and customer service functions. Machine learning can guide routing, inventory placement, and maintenance planning with less manual intervention. As delivery expectations rise, such tools can help companies improve speed, control costs, and strengthen service quality.
For instance, in May 2026, Logility emphasizes decision-centric, AI-powered orchestration that keeps supply chains moving forward. Event logistics managers using Logility can lean on predictive and automated planning to adjust sourcing, transport, and inventory positions as ticket sales, weather, and supplier signals change closer to the event date.
The skills gap is a key challenge for machine learning adoption in logistics. Many companies need employees who understand both logistics operations and data-driven systems. Without trained teams, it becomes difficult to build, manage, and apply machine learning models in daily workflows.
Operational staff may also hesitate to trust automated recommendations if the system is not clearly explained. Training, governance, and gradual adoption are needed to build confidence. Firms that fail to develop internal skills may struggle to scale machine learning beyond small pilot projects.
For instance, in August 2025, Manhattan’s AI-driven capabilities require customers to adopt new ways of working, often supported by training and change programs. Event logistics operations, especially those with seasonal staff, face a challenge in embedding these skills across teams, potentially slowing the shift from manual to AI-assisted decisions.
One of the leading players in December 2025, IBM expanded its WatsonX-based optimization and forecasting tools for supply chain planning, packaging them with industry blueprints for logistics, retail, and manufacturing clients in North America. This move underpins IBM’s role in machine-learning-driven demand sensing, route planning, and inventory optimization across complex global networks.
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