Crime analysis is a critical aspect of law enforcement, aiding in the understanding, prediction, and prevention of criminal activities. In a vast and diverse country like India, with its complex socio-economic landscape, traditional methods of crime analysis often fall short in capturing the intricacies and patterns of criminal behavior. In recent years, machine learning (ML) and deep learning (DL) techniques have emerged as powerful tools to analyze crime data, offering the potential to uncover hidden patterns and trends that can enhance law enforcement strategies. This paper presents a comprehensive overview of crime analysis in India utilizing machine learning and deep learning methodologies. We begin by discussing the challenges inherent in traditional crime analysis methods, highlighting the need for more sophisticated approaches to address the complexities of crime dynamics in India. Subsequently, we delve into the theoretical foundations of machine learning and deep learning, providing insights into various algorithms and techniques commonly employed in crime analysis. Drawing upon real-world datasets from Indian cities, we demonstrate the application of machine learning and deep learning techniques in crime prediction, hotspot identification, and criminal profiling.