Recent years have seen the production of a broad range of polymers by the petrochemical sector which have tremendously helped human development. On the other hand, improper disposal of plastic has gravely harmed ecosystems, animals and resource sustainability by means of pollution. Given its long decomposition time and consistent character plastic waste management is a serious global problem. Recycling helps to reduce virgin plastic production, lessen environmental impact and support circular economy initiatives. Still manual plastic waste sorting is physically taxing and economically unproductive. Using hyperspectral photography, the proposed method accurately identifies and separates plastic waste. Initially, it separates plastic waste from regular waste in disposal sites using object recognition and hyperspectral image classification. After that, the separated waste is handled by a multi-layered sensor array designed to identify various plastic materials depending on unique spectral fingerprints. This approach exactly separates recyclable materials from non-recyclable polymers. While recyclable plastics are further categorized by type for efficient processing, non-recyclable plastics are under control to lower environmental damage. By means of reduction of their footprint, this system aims to increase recycling efficiency and minimize the environmental impact of plastic waste by using an automated, scalable and efficient framework for plastic waste management.