Recommendation system is very important in e-commerce, entertainment, education, and healthcare. It is to help users to find about relevant content. The recommendation systems analyse its attributes and user preferences to create opinions. Although, current methods face lots of problems like cold-start problem, filter bubbles, lack of adaptability, data sparsity, and high dependency. It is often relied on correlation-based learning, it can reinforce biases and repetitive recommendations, and it leads to underprivileged personalization. To address these issues this research proposed a state-of-the-art content-based recommendation system that engages Ontology-Based Inferences with Hybrid Casual Representation Learning (CRL) and Reinforcement Learning (RL). The goal is to enhance recommendation accuracy, fairness, and responsiveness by applying ontology for knowledge representation in a structured manner, CRL for identifying true cause-effect relationships in user preferences, and RL for real-time adaptive learning. The novelty of the method lies in its ability to reduce bias, boost diversity in recommendations, and dynamically respond to shifts in user interests. In order to check the efficiency of the proposed approach, conducted experiments with benchmark datasets and compared it to traditional content-based approaches. The results indicate that method considerably enhances recommendation accuracy, personalization, and user satisfaction and surpasses the drawbacks of current methods efficiently. Experiments were conducted using the Amazon Reviews 2023 dataset, and the model was implemented using Python, TensorFlow, and Scikit-Learn. The findings indicate that the proposed method outperforms traditional content-based approaches, showing a 12% improvement in accuracy, higher personalization, and increased user satisfaction. These results confirm that integrating ontology, CRL, and RL creates a more intelligent, unbiased, and adaptive recommendation system, effectively overcoming the limitations of existing methods.