Effective segregation is now a critical component of urban environmental management, particularly in emerging regions, due to the sharp rise in municipal solid waste. This study presents a low-cost, sensor-driven intelligent waste sorting system that uses an infrared sensor, rain-drop moisture sensor, and inductive proximity sensor connected to an Arduino-based control unit to automatically classify household waste into metallic, wet, and dry non-metal categories. 30 waste samples were used in the system's experimental evaluation, which produced a total classification accuracy of 76.67%, 80% accuracy for metal and dry trash, and an improvement in wet waste accuracy from 60% to 70% after sensor recalibration.

At a total cost of 4320 BDT, the prototype operated with perfect hardware reliability, demonstrating its suitability for environments with low resources, such as households, universities, and small businesses. By reducing the need for manual sorting and minimizing direct human contact with mixed garbage, the proposed method increases worker safety, improves the quality of recyclable material recovery, and encourages more sustainable solid waste management practices.