Urban waste is growing at an unsustainable pace, posing major challenges for environmental quality, public health, and municipal planning especially in fast-urbanizing regions. This study presents a data-driven framework that combines emissions forecasting, waste-material prioritization, and a digital decision-support platform to inform smarter, more equitable waste policies. Using publicly available U.S. EPA datasets and WARM emissions factors, we built a linear regression model to estimate greenhouse gas (GHG) emissions from landfilled materials through 2030. Results show that food waste remains the fastest-growing contributor to landfill-related GHG emissions, while paper and metals offer significant mitigation potential through recycling. These insights were used to develop an interactive application piloted in Lagos, Nigeria, incorporating localized waste generation data, per-capita estimates, GIS inputs, and route optimization features. The tool enables users, government agencies, NGOs, and community leaders, to simulate scenarios, assess trade-offs, and identify high-impact interventions. Although grounded in U.S.-based emission factors, the app’s structure allows future adaptation using regional data from other countries. This project bridges the gap between technical analysis and real-world applications, demonstrating a scalable approach for advancing climate resilience, health equity, and low-carbon urban development through improved waste system design.