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In the rapidly digitalizing world, the management of electronic waste (e-waste) has emerged as a pressing concern, especially in developing nations like India. This research introduced a dynamic e-waste collection system in rural India, leveraging the Internet of Things (IoT), strategic management, and machine learning. The pilot implementation witnessed a remarkable threefold increase in monthly e-waste collection, soaring from 150 to 420 tonnes. Furthermore, the average collection time was slashed from 10 days to just 3, while community awareness about e-waste management surged from 30% to 78%. Utilizing machine learning models like ARIMA, geospatial analysis, and Prophet, the system adeptly forecasted e-waste generation patterns, thereby optimizing collection strategies. Despite its promising results, challenges such as IoT infrastructure in remote areas and varying rural landscapes necessitate further refinements. The study not only underscores the potential of technology and strategy in addressing e-waste challenges but also highlights a scalable model that can be adapted for similar regions globally, paving the way for sustainable waste management and environmentally friendly development. [ABSTRACT FROM AUTHOR]