The collection of real-time data from people, their cars, public transit, buildings, and other urban infrastructures like the energy grid and waste management systems are at the heart of the smart city concept. The insights gathered from the data may be used by municipal authorities to manage resources and services efficiently. An important study topic at the same time is the sharp increase in environmental degradation and deterioration that causes ecological imbalance. Additionally, the development of advanced waste management systems that can categorize rubbish according to its level of biodegradability is required for the worldwide expansion of smart cities. Some of the more common ones are paper, paper boxes, food, glass, and other garbage. A cost-effective technique to separate the waste from the enormous mountain of trash and garbage and classify the waste items is to use computer vision-based technologies. Recent developments in deep learning (DL) and deep reinforcement learning (DRL) have made it possible to categorize waste objects and identify and detect trash. In this sense, the research creates an intelligence model for smart cities. The technique's goal is to recognize and classify rubbish objects using the DL and DRL approaches. The two steps of the SYSTEM technique are objected classification based on DRL and object detection based on Mask Regional Convolutional Neural Network (CNN)… The CNN model uses the DenseNet model as a baseline model, and a deep learning network (DLN) is employed as the classifier. A based hyperparameter optimizer is also created to boost the efficiency of the DenseNet model.