Rapid urbanization has intensified the challenge of municipal solid-waste (MSW) collection and disposal. Existing rule-based scheduling leads to overflow incidents, unnecessary vehicle mileage, and excessive carbon emissions. We propose a unified Internet-of-Things (IoT) and artificial-intelligence (AI) framework that ingests real-time bin-fill data from wireless ultrasonic sensors, forecasts short-term generation with an LSTM network, and optimizes collection routes through a reinforcement-learning (RL) scheduler. A four-city case study (Jaipur, Kota, Udaipur, Ajmer) shows a 29–38% reduction in monthly waste volume delivered to landfills, 35% fewer overflow events, and 27% lower fuel use after three months of deployment. The framework is lightweight enough for edge devices (Raspberry Pi 4 B) and supports open-data dashboards for transparent e-governance. These findings illustrate how data-driven MSW management can accelerate Sustainable Development Goal 11 while remaining cost-effective for mid-sized Indian municipalities.