Images serve a vital role and transmit a wealth of information. Finding important image information in a fair period of time is critical for many photos. A variety of factors influence image categorization results, including the algorithm's remarkable performance. To categorise an image, first input it into a particular classification technique. Preprocessing photos, identifying features, and building classifiers comprise the majority of image classification. The deep learning model's convolutional neural network can automatically extract local features and share weights, which is an advance above traditional machine learning's manual feature extraction. Picture classification produces better outcomes than more traditional machine learning algorithms. This study focuses on image categorization algorithms based on convolutional neural networks. Deep belief network approaches are also contrasted and evaluated, and the study finishes with an overview of the algorithms' application aspects.