Conversations are complex and challenging to analyze due to their inherent variability in data, context, and user influence, making traditional NLP approaches less effective. The lack of clear structure and hierarchical meaning in conversations adds to the difficulties, whereas deep learning models, although powerful for certain tasks, struggle with the train-test mismatch and context injection. Understanding conversation intelligence requires addressing these challenges and exploring hybrid learning approaches that can effectively handle uncertainty and incomplete information.