This perspective article explores how modern machine learning techniques, such as convolutional neural networks and transformer architectures, can accelerate the analysis and control of chaotic dynamics. It highlights their potential to overcome the computational limitations of traditional methods in tasks like basin characterization and partial control, opening doors for real-time applications.