Environmental pollution resulting from waste is a critical global challenge that significantly affects both the environment and public health, especially in countries like Indonesia. Effective waste management and recycling depend on accurately detecting and classifying different waste types. This study tackles this challenge by evaluating the YOLOv8s algorithm for object detection and conducting a comparative analysis of two mobile-optimized convolutional neural networks (CNNs), MobileNetV2 and EfficientNet, for waste classification. The YOLOv8s model established a promising baseline for detection, achieving a mean Average Precision (mAP@50) of 0.621 on the hold-out test set. MobileNetV2 proved to be the superior architecture in the classification task, attaining a higher accuracy of 94.4% compared to EfficientNet’s 87.8%. Additionally, MobileNetV2 demonstrated significantly greater computational efficiency, with a processing time of 229 ms per step, in contrast to EfficientNet’s 606 ms per step. These findings confirm that combining YOLOv8s for detection and MobileNetV2 for classification provides a robust and efficient pathway for developing automated waste management systems.