Underwater sonar systems are critical for various applications including national security, commercial exploration, environmental monitoring, and search and rescue operations. These systems, which operate using sound propagation to detect and classify underwater objects, face significant challenges due to the complex and noisy nature of the underwater environment. Traditional sonar data processing methods often fall short in terms of accuracy and efficiency. This paper reviews the integration of machine learning (ML) techniques with sonar systems to enhance underwater target prediction. We discuss the types and applications of sonar systems, emphasizing the importance of accurate target detection and classification. The role of ML in improving sonar capabilities is explored, highlighting its contributions to automated feature extraction, handling complex data, and real-time adaptive learning. By leveraging advanced ML algorithms, sonar systems can achieve greater precision, robustness, and adaptability, leading to significant advancements in underwater exploration and safety. This review aims to provide a comprehensive understanding of current methodologies and future directions in the field of sonar target prediction using machine learning. The review begins with an overview of sonar systems, detailing their operational principles and various applications. It then underscores the critical importance of accurate underwater target detection and classification for ensuring national security, enabling effective resource management, and protecting marine environments. The core of the paper examines how machine learning methodologies are revolutionizing sonar capabilities. By automating feature extraction and selection, handling noisy and high-dimensional data, and enabling real-time adaptive learning, ML algorithms significantly outperform traditional approaches.