In recent years, machine learning (ML) has emerged as a powerful tool for predicting the mechanical behavior of composite materials, offering a faster, more cost-effective alternative to traditional testing and simulation methods. This review explores how various ML techniques, including random forests, support vector machines, artificial neural networks, and deep learning models, are used to forecast key material properties such as tensile strength, hardness, fracture toughness, and fatigue life. From a broad spectrum of recent studies, the article highlights how ML models are trained on experimental and simulation data to explore complex relationships between processing parameters, material compositions, and resulting performance. The review also compares the accuracy, limitations, and suitability of different algorithms and emphasizes the growing importance of hybrid models, feature selection, and data quality. Finally, the future potential of ML in multiscale modeling, design optimization, and real-world applications, while also addressing practical challenges like data scarcity, model interpretability, and the need for standardized validation protocols, has been discussed. By summarizing current progress and challenges, the article supports informed decision-making in adopting ML for composite materials research and application.