Today’s international economy have been considering Supply chain efficiency and resilience at the core to enable competitive advantage throughout the market. In this research we have been trying to probe the application of Predictive Analytics considering different Machine Learning techniques to aid Supply Chain Management (SCM) by enabling more thoughtful decision-making, minimizing disruptions, and optimizing efficiency in operations. Applying supervised ML techniques such as Logistic Regression, Support Vector Machines, Naive Bayes, and k- Nearest Neighbors, we have put efforts to analyze historical supply chain information to forecast product performance and measure transportation efficiency. We gave identified the skincare as the top-performing category with 40% sales and cite speed and cost balance offered by road and rail transportation. However, we have also observed that the predictive models hold immense promise, issues such as data silos, algorithmic bias, and the cost of deployment continue to exist and deter large-scale deployment, defining SMEs specifically. This research proposes a low-cost and viable framework for PA deployment in SCM and offers strategic recommendations to guide inventory optimization, customer segmentation, and transportation planning, with predictive accuracy of approximately 78%. This study also puts some perspective towards the paradigm-shifting nature of data-driven methods in modern supply chain settings.