Waste management is a major environmental challenge faced by modern society due to rapid urbanization and population growth. Improper segregation of waste results in pollution, health hazards, and inefficient recycling processes. This paper presents the design and implementation of an Automated Waste Segregation System that automatically separates waste into wet, dry, and metallic categories using sensors and a microcontroller. The proposed system uses a moisture sensor to identify wet waste, a metal sensor to detect metallic objects, and a control unit to classify dry waste. Based on sensor inputs, a motor-driven mechanism directs the waste into the appropriate bin. This system minimizes human intervention, improves waste handling efficiency, and supports sustainable waste management practices. The project is cost-effective, easy to implement, and suitable for applications such as smart cities, public places, and residential areas. Solid waste management has become a critical challenge due to rapid urbanization and increasing population. Improper segregation of waste at the source results in environmental pollution, health hazards, and inefficient recycling processes. This paper presents the design and implementation of an Automated Waste Segregation System that separates waste into wet, dry, and metallic categories using sensors and a microcontroller. The proposed system utilizes a moisture sensor to identify wet waste and a metal sensor to detect metallic objects, while dry waste is classified by elimination. An Arduino-based control unit processes the sensor inputs and actuates servo motors to direct waste into the respective bins. The system reduces human intervention, improves segregation accuracy, and ensures hygienic waste handling. Experimental results indicate that the system is efficient, cost-effective, and suitable for small-scale applications such as residential areas, institutions, and public places. The proposed solution supports sustainable waste management practices and can be further enhanced with IoT-based monitoring and intelligent classification techniques.