Machine learning (ML) is transforming molecular dynamics (MD) simulations by addressing key challenges such as insufficient sampling, inaccurate force fields, and complex trajectory analysis. MD simulations provide insights into atomic-level processes, but traditional methods face limitations in accuracy, efficiency, and interpretation. Recent advances in AI, particularly deep learning (DL), offer promising solutions. ML-based force fields, enhanced sampling techniques, and innovative trajectory analysis methods are improving the accuracy and efficiency of MD simulations. However, challenges remain, including the need for large datasets, computational demands, and the integration of ML with AI. This review highlights the integration of DL with MD simulations, discussing recent developments in ML-based force fields, conformational sampling, and trajectory analysis. It also addresses the challenges and implications of ML-MD fusion, emphasizing the potential for future advancements in MD simulations. The review provides a comprehensive overview of the new perspectives ML has introduced in MD, serving as a foundation for further research and development in this exciting field.Machine learning (ML) is transforming molecular dynamics (MD) simulations by addressing key challenges such as insufficient sampling, inaccurate force fields, and complex trajectory analysis. MD simulations provide insights into atomic-level processes, but traditional methods face limitations in accuracy, efficiency, and interpretation. Recent advances in AI, particularly deep learning (DL), offer promising solutions. ML-based force fields, enhanced sampling techniques, and innovative trajectory analysis methods are improving the accuracy and efficiency of MD simulations. However, challenges remain, including the need for large datasets, computational demands, and the integration of ML with AI. This review highlights the integration of DL with MD simulations, discussing recent developments in ML-based force fields, conformational sampling, and trajectory analysis. It also addresses the challenges and implications of ML-MD fusion, emphasizing the potential for future advancements in MD simulations. The review provides a comprehensive overview of the new perspectives ML has introduced in MD, serving as a foundation for further research and development in this exciting field.