S. Puneeth, A. Aravind Goud, M. Sreeram, K. Thirumalesh, K. Venkatesh 02 08 The growing demand for efficient ridesharing and food delivery services has necessitated innovative solutions to accurately predict and manage driver demand These services rely heavily on timely driver availability to meet customer expectations and ensure operational efficiency Historically driver demand in such industries has been addressed using manual forecasting methods and basic statistical models However these approaches often fall short in capturing the dynamic and unpredictable nature of demandespecially during peak hours special events or sudden environmental changes Traditional systems limited by their inability to incorporate realtime data have led to delays customer dissatisfaction and inefficient resource allocation Before the advent of AI these challenges were tackled using rudimentary scheduling systems fixed staffing policies and reactive strategies that failed to adapt to rapidly changing conditions These shortcomings combined with the rapid growth of ondemand services highlighted the urgent need for robust datadriven solutions This research is motivated by the limitations of traditional forecasting methods and aims to leverage advanced machine learning techniques to improve demand prediction Inspired by the success of AIdriven systems in addressing complex nonlinear problems across various industries the proposed system utilizes ensemble learning techniques and timeseries analysis to forecast driver demand accurately By incorporating historical trends external variables such as weather and events and realtime data the system delivers precise forecasts that enable proactive resource management This research not only enhances operational efficiency but also improves customer satisfaction by reducing wait times and optimizing driver allocation in ridesharing and food delivery platforms Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026