Machine learning review maps 78 studies on inventory forecasting and market trends

Machine learning techniques are increasingly being used to improve demand forecasting and inventory management, according to a systematic review published in Artificial Intelligence Review. The review, made available as an accepted open-access article ahead of its final version of record, examines how these methods and trend analysis can support forecasting accuracy, reduce uncertainty, and inform inventory decisions in the face of rapid market changes, shorter product life cycles, and supply-chain disruptions.

The authors screened an initial pool of 116,377 studies and systematically selected 78 papers for detailed analysis. The review covers supervised, unsupervised, and reinforcement learning approaches, as well as deep learning, hybrid forecasting methods, and emerging quantum machine learning techniques.

Among the models identified as widely used for demand forecasting and inventory optimization are Random Forest, XGBoost, LSTM networks, and Temporal Convolutional Networks. The review also reports that reinforcement-learning methods support adaptive inventory control, allowing inventory policies to adjust as conditions change.

Beyond the core modeling landscape, the study highlights recent advances in explainable AI, resilience-aware optimization, and sustainability-focused inventory systems.

The review also discusses challenges that continue to limit the application of machine learning in this domain, including data sparsity, explainability, and governance.

As an early-access publication, the article is citable and carries a permanent DOI, but the publisher notes that this version is subject to further edits and will be replaced by the final version of record. Readers should therefore treat the findings as those of a peer-reviewed, accepted systematic review rather than a finalized published edition.

Subject of Research: A systematic review of machine learning applications in inventory management and demand forecasting

Article Title: Leveraging machine learning in balancing inventories and analyzing market trends: a comprehensive systematic review

Article References: Hossain, M. S., Haque, M. S., Razzaque, Z. I., Rahman, M. M., Mousum, M. M. M., Robin, R. A., Noor, J., & Huda, M. N. (2026). Leveraging machine learning in balancing inventories and analyzing market trends: a comprehensive systematic review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-026-11723-2

Image Credits: AI Generated

DOI: 10.1007/s10462-026-11723-2

Keywords: machine learning, inventory management, demand forecasting, systematic review, deep learning, reinforcement learning, supply chain, XGBoost, LSTM, explainable AI, Leveraging, machine