The integration of machine learning, and particularly deep learning, with active matter research provides new possibilities for research and innovation. Active matter systems, which encompass a broad range of natural and artificial entities that consume energy to perform mechanical work, present unique challenges due to their intrinsic out-of-equilibrium dynamics. Recent advancements in deep learning, offer unprecedented opportunities to analyze, model, and understand these complex systems. By addressing both the opportunities and challenges, including the need for physics-informed models and the reality gap between simulations and real-world applications, this chapter highlights the mutual benefits of combining machine learning with active matter research.