  This paper explores the prediction of subsequent steps in H\'enon Map using
various machine learning techniques. The H\'enon map, well known for its
chaotic behaviour, finds applications in various fields including cryptography,
image encryption, and pattern recognition. Machine learning methods,
particularly deep learning, are increasingly essential for understanding and
predicting chaotic phenomena. This study evaluates the performance of different
machine learning models including Random Forest, Recurrent Neural Network
(RNN), Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM),
and Feed Forward Neural Networks (FNN) in predicting the evolution of the
H\'enon map. Results indicate that LSTM network demonstrate superior predictive
accuracy, particularly in extreme event prediction. Furthermore, a comparison
between LSTM and FNN models reveals the LSTM's advantage, especially for longer
prediction horizons and larger datasets. This research underscores the
significance of machine learning in elucidating chaotic dynamics and highlights
the importance of model selection and dataset size in forecasting subsequent
steps in chaotic systems.
