Cracks in surfaces like concrete structures, pavements, and industrial components present significant challenges concerning safety, maintenance, and structural integrity. Traditional crack detection methods typically involve manual inspections, which are time-consuming, costly, and prone to human error. This research paper introduces an innovative approach to crack detection on surfaces using advanced machine learning techniques. It investigates the use of cutting-edge machine learning algorithms, such as convolutional neural networks (CNNs) and deep learning models, to automate crack identification across various surface materials. The proposed methodology encompasses key steps: collecting data via high-resolution imaging techniques, preprocessing data to enhance image quality and reduce noise, and training deep learning models with annotated crack datasets. Existing literature highlights various methods for automatic crack detection and depth estimation using image processing techniques. This research conducts a thorough survey to pinpoint current challenges and advancements in this field. Relevant research papers on crack detection are carefully selected and reviewed, focusing on the image processing techniques used, research objectives, achieved accuracy levels, error rates, and the diversity of image datasets. This paper offers a comprehensive review of image-based crack detection techniques that integrate image processing and machine learning approaches.