Human behavior correct modeling is very essential for next gen intelligent system, but it still has a lot of practical difficulties to be addressed even though it can deal with high dimensional data better than traditional Machine Learning (ML). Multimodal data fusion, loss of long-term temporal information, lack of interpretability. These are the problems. This paper looks into how deep learning is used in three main parts: feeling figuring out, social interaction patterns, and everyday actions watching. These findings reveal a distinct ket-set: structures like Long Short-Term Memories (LSTMs) and Convolutional Neural Netwroks (CNNs), they kasper tasks simple things really good, their ability add the exact randomness catching real societies is harder though. As well as that, Graph Neural Networks (GNNs) can model relationships but have computational scalability issues. Finally, this study recommended using Explainable Artificial Intelligence (XAI) along with privacy preserving method to solve the problem of “black box” and fill the gap between practical performance and reliable application of XAI.