Using Machine Learning Towards Evaluation of Environmental Impacts and Mitigation in Sustainable Supply Chain Management

The intersection of Predictive analyze machine learning (ML), and sustainable supply chain management (SSCM) creates a dynamic and inventive link in today's quickly changing business environment. Attaining this agility requires proactive risk mitigation. This investigation proposes a way to improve efficiency and improve risk mitigation by combining ML and predictive analytics methods.This study looks at a variety of predictive analytics techniques and demonstrates how supply chain hazards can be identified using them. These techniques include natural language processing, anomaly detection, and time series analysis. Furthermore, machine learning algorithms are used to continuously refine and optimise risk assessment models, guaranteeing their accuracy and flexibility in ever-changing circumstances. This study synthesizes theoretical discourse and empirical data to provide light on the mutually beneficial link between supply chain agility, machine learning, and predictive analytics.This investigation proposes a way to improve efficiency and improve risk mitigation by combining ML and predictive analytics methods. One of the key accomplishments is the creation of an extensive framework that integrates machine learning and predictive analytics into risk management practices, allowing for the identification and mitigation of risks.