Molecular dynamics (MD) simulations are a crucial computational chemistry technique that provides insights into atomic-level processes. However, MD faces limitations such as insufficient sampling, inadequate accuracy of atomistic models, and challenges in trajectory analysis. The integration of machine learning (ML) and deep learning (DL) offers promising solutions to these issues. This review highlights the basics of MD, its limitations, and the integration of ML into MD simulations. Key advancements include the development of ML-based force fields, techniques for improved conformational space sampling, and innovative methods for trajectory analysis. The review also discusses the challenges and implications of ML-aided MD, emphasizing the need for further applications to confirm its superiority over traditional methods. The integration of AI techniques in MD simulations can be approached from both algorithmic and methodological perspectives, with open-source tools and frameworks available for developers and researchers. The application of ML in MD provides more flexibility, tractability, and better scalability when studying high-dimensional data. Recent advances in ML-based force fields, such as DCF, SchNet, GNNFF, TorchMD, ACEMD3, CGSchNet, ANI, and np2p NNP, have shown significant improvements in accuracy and efficiency. Additionally, ML techniques can support enhanced and adaptive sampling methods, aiding in the exploration of complex molecular systems. Overall, the integration of ML and DL in MD simulations represents a new phase of development, offering exciting opportunities for future research and practical applications.Molecular dynamics (MD) simulations are a crucial computational chemistry technique that provides insights into atomic-level processes. However, MD faces limitations such as insufficient sampling, inadequate accuracy of atomistic models, and challenges in trajectory analysis. The integration of machine learning (ML) and deep learning (DL) offers promising solutions to these issues. This review highlights the basics of MD, its limitations, and the integration of ML into MD simulations. Key advancements include the development of ML-based force fields, techniques for improved conformational space sampling, and innovative methods for trajectory analysis. The review also discusses the challenges and implications of ML-aided MD, emphasizing the need for further applications to confirm its superiority over traditional methods. The integration of AI techniques in MD simulations can be approached from both algorithmic and methodological perspectives, with open-source tools and frameworks available for developers and researchers. The application of ML in MD provides more flexibility, tractability, and better scalability when studying high-dimensional data. Recent advances in ML-based force fields, such as DCF, SchNet, GNNFF, TorchMD, ACEMD3, CGSchNet, ANI, and np2p NNP, have shown significant improvements in accuracy and efficiency. Additionally, ML techniques can support enhanced and adaptive sampling methods, aiding in the exploration of complex molecular systems. Overall, the integration of ML and DL in MD simulations represents a new phase of development, offering exciting opportunities for future research and practical applications.