Semi-urban regions in India are increasingly experiencing freshwater scarcity, exacerbated by inefficient domestic greywater utilization. While urban centers benefit from centralized water recycling infrastructure, decentralized solutions for low-income and semi-urban communities remain underdeveloped. This study proposes an intelligent greywater recycling system that integrates Internet of Things (IoT) sensors and Artificial Intelligence (AI) to optimize water reuse at the household cluster level. A low-cost sensor network continuously monitors key water quality parameters-pH, turbidity, and flow rate-transmitting real-time data to a machine learning (ML) model. The model predicts contamination thresholds and recommends appropriate treatment or reuse strategies, ensuring safe and efficient water recycling. A simulated case study in a semi-urban residential colony in Punjab demonstrates the system’s efficacy, achieving a 30% reduction in potable water demand while minimizing operational inefficiencies and maintenance delays. The proposed architecture supports edge and cloud computing, enhancing scalability and reliability in resource-constrained environments. Performance validation through data analytics, cost–benefit assessment, and system simulations confirms its feasibility and sustainability. This solution aligns with India’s Jal Jeevan Mission and United Nations Sustainable Development Goal 6 (Clean Water and Sanitation), offering a scalable, energy-efficient, and economically viable approach to decentralized water management.