In the current world, the growing waste has become a major problem; the only solution is waste recycling. The crucial step in this process is segregating the waste into recyclable and organic waste. With growing concerns over improper waste disposal, automating the segregation process can significantly enhance the efficiency of recycling systems and reduce human intervention. The proposed method integrates Convolutional Neural Networks (CNNs), XG Boost, and Random Forest to segregate the waste into two classes: recyclable waste (such as plastic, glass, and metals) and organic waste (such as food and biodegradable materials). CNNs are highly effective in image classification tasks. We use XG Boost to model more complex relationships between features that may not be purely visual. To enhance robustness, Random Forest is employed to build multiple decision trees and aggregate their outputs for final classification. The automation of this process reduces the reliance on manual sorting, making waste management systems more efficient and environmentally sustainable.