Dr.M.Ramakanth Reddy,R.Srilekha,Mallani Niharika 02 05(1) This study delves into the evolution of Working Capital Management WCM strategies by integrating traditional financial methodologies with advanced Machine Learning ML and Deep Learning DL techniques Recognizing the critical role of efficient WCM in ensuring a firms liquidity profitability and sustainable growth especially within the context of seasonal fluctuations supply chain complexities and evolving economic landscapes in India this research aims to provide a comprehensive framework for optimizing working capital The study will first establish a baseline using conventional WCM approaches focusing on the management of cash inventory accounts receivable and accounts payable and their impact on the Cash Conversion Cycle CCC It will then explore how ML algorithms such as Random Forest and Gradient Boosting can be applied to large historical datasets to identify the most significant internal and external factors influencing working capital needs thereby improving forecasting accuracy for key components like sales demand inventory levels and payment patterns Furthermore Deep Learning models particularly Long ShortTerm Memory LSTM networks will be deployed to capture complex temporal dependencies and nonlinear relationships in highly volatile financial data enabling more precise realtime cash flow and working capital requirement predictions The performance of these AIdriven models will be rigorously evaluated against traditional forecasting methods eg ARIMA regression analysis using metrics like Mean Absolute Percentage Error MAPE and Root Mean Squared Error RMSE Ultimately this research seeks to demonstrate how the synergy between established WCM principles and cuttingedge AI technologies can lead to superior liquidity management reduced financing costs minimized stockouts or excess inventory and enhanced overall financial resilience for businesses in India Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026