Volume 2 - Issue 3, May - June 2026

Agribusiness enterprises operate in highly volatile markets characterized by commodity price fluctuations, perishability risks, seasonal production cycles, and limited access to credit, all of which threaten financial sustainability. Machine learning (ML) offers advanced capabilities for extracting predictive insights from large and complex datasets, thereby improving market forecasting, financial risk assessment, and enterprise decision-making. This paper reviews the application of machine learning in enhancing market forecasting and financial sustainability within agribusiness enterprises. The review covers commodity price prediction, demand and supply chain forecasting, credit risk assessment, financial distress prediction, firm performance evaluation, and sentiment analytics. A systematic narrative review was conducted using peer-reviewed literature sourced from Scopus, Web of Science, PubMed, Nature, Frontiers, Springer, ScienceDirect, Emerald, and the Consensus academic search repository. Thirty high-quality studies spanning agricultural economics, financial analytics, supply chain management, and machine learning were synthesized. The findings indicate that hybrid deep learning models outperform traditional forecasting approaches in predicting agricultural commodity prices. Long Short-Term Memory (LSTM) models consistently surpass ARIMA models in forecasting accuracy. Ensemble machine learning techniques, including Rotation Forest and Logit Boosting, effectively assess credit risk among agricultural SMEs, with financial leverage, current ratio, profit margin, and sales growth identified as key predictors. Machine learning-based demand forecasting improves supply chain efficiency and inventory management, while sentiment analysis using natural language processing enhances market intelligence by incorporating signals from news and social media sources. Overall, machine learning is transforming agribusiness management by strengthening market forecasting, financial risk management, and supply chain optimisation. However, challenges related to data governance, model interpretability, and equitable access remain critical areas for future attention.

Micheal Ihonre,"Using Machine Learning to Enhance Market Forecasting and Financial Sustainability of Agribusiness Enterprises" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(3): Page(1045-1052) May-June 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.