A subfield of computer vision called image recognition analyzes and interprets visual data using machine learning and artificial intelligence. Objects, people, colors, forms, messages, and emotions are just a few of the aspects that image recognition algorithms can identify and detect. Additionally, image recognition is capable of segmenting, localizing, tracking, and classifying images—tasks that are critical to surveillance systems. The study on object identification in addition to image classification is compiled in this publication. The combination of Artificial Intelligence (AI) and Machine Learning (ML) models might led to notable improvements in image recognition and classification. By making it possible to automatically, effectively, and precisely identify patterns, objects, and characteristics in images, these technologies have completely transformed conventional image analysis. In order to improve picture recognition and classification, this study investigates the use of AI-driven models, as well as deep learning architectures similar to Convolutional Neural Networks (CNNs), Transfer learning, and Vision Transformers (ViTs). The study looks at several machine learning algorithms, how well they work in various fields like healthcare, security, and autonomous systems, and the difficulties with dataset quality, processing demands, and model interpretability. We also go into performance improvement strategies and the potential applications of AI-powered image analysis. The results show that using AI and ML models greatly increases real-world adaptability, detection accuracy, and classification efficiency, making them essential tools for contemporary computer vision applications.