This paper provides an overview of the advancements and challenges in image recognition technology that utilizes machine learning. Image recognition involves identifying and categorizing objects in images, with the primary goal of determining the best algorithm for accurately classifying these objects. The paper outlines essential processes in image recognition, such as data acquisition, preprocessing, feature extraction, and classifier design, highlighting the importance of feature extraction and selection for improving accuracy. A comparison of ML and DL techniques, as well as algorithms used in image recognition, is discussed, along with the classification of images using machine learning. The aim is to identify the processes, models, and algorithms most suitable for classifying and recognizing detected objects. Furthermore, the research explores the evolution of image recognition, particularly through the combination of ML and DL techniques. By examining the broad applications of ML in this field, the paper aims to offer insights that can enhance the development of machine learning applications in image recognition, thereby improving the effectiveness of image processing technologies by identifying the best algorithms and processes.