Rapid Urbanization and the growing volume of  Municipal Solid Waste pose Significant Challenges to Sustainable Waste Management, especially in Developing Regions. A key barrier is the absence of effective source segregation, which often results in Contamination of  Recyclables, Reduced Recycling Efficiency, and Increased reliance on Landfills. To address this issue, this paper presents the Design and Development of an Intelligent Automated Waste Segregation System capable of Classifying Waste into four categories: Biodegradable(Wet), Dry, Electronic and Unsorted(Residual). The proposed system integrates Sensors, Computer Vision (CV) and Microcontroller-based control, complemented by AI-driven Image recognition to improve classification Accuracy and Reliability. The Sensor module utilizes a Moisture Sensor for identifying Biodegradable Waste, Capacitive Sensor for detecting Dry Waste and an Inductive Sensor for recognizing Electronic or Metallic Waste, thereby Enhancing precision in Waste Categorization. A modular Carousel-disc Mechanism with detachable compartments ensures Efficient Segregation and Simplifies Disposal. In addition, optional IoT connectivity allows for real-time Monitoring and Data Analytics, enabling integration with Smart City Infrastructure. Experimental Validation demonstrates promising classification accuracy and confirms the system’s scalability for Households, Institutions and Public Facilities. By Automating Segregation at the point of disposal, the System reduces Human intervention, Improves Recycling Efficiency and Enhances the safe handling of hazardous waste. Overall, this work presents a practical, adaptable, and technology-driven approach to Waste Management, contributing to the Development of more Sustainable and responsible Urban Ecosystems.