The rapid growth of the film industry and the increasing availability of digital content have posed significant challenges in terms of movie content rating and personalized recommendation systems. This research presents an innovative and advanced approach to address these challenges by leveraging artificial intelligence (AI) techniques. Theprimary objective of this research is to develop an AI-based movie content rating and recommendation system that enhances user experience and promotes content diversity. The research proposes the implementation of a dynamic intelligent conversational chatbot system that interacts with users, allowing them to provide feedback, receive personalized recommendations, and obtain detailed information about movies. By employing natural language processing and machine learning algorithms, the chatbot system adapts to user preferences, learning from past interactions and continuously improving the quality of recommendations. Additionally, the research explores the field of sentiment analysis in Singlish, a unique language variant, to accurately assess the emotional tone of movie reviews. This analysis helps in understanding user sentiments towards specific movies and assists in fine-tuning the recommendation system. Furthermore, this research investigates the task of video-based movie genre classification, where research employs deep learning techniques to automatically assign genres to movies based on their visual content. This approach allows for more accurate and efficient genre tagging, which is essential for both content rating and recommendation purposes. Lastly, this research proposes a novel method for movie content-based video retrieval from short video clips, utilizing deep learning architectures to extract relevant information and enable efficient searching within a movie.