Chaos and nonlinear dynamical systems are important for understanding complicated things that happen in biology and physics. The complexity and sensitivity to starting conditions of these systems make it hard for traditional analysis methods to work with them. In this study used Machine Learning and Deep learning Based method, to successfully look at and predict chaotic and nonlinear behaviours. After data is gathered from real-world and computer-simulated biological and physical systems, it is preprocessed and features are extracted in a way that works best for time-series and spatial datasets.