The output generation in large language models (LLMs) creates factually incorrect and fabricated or inconsistently logical data. The solution of hallucination represents a key roadblock mainly among datasets containing medical content including text together with audio and video elements and images. This proposed framework based on CNN used to handle hallucination through sequence logic conflict resolution together with perceptual ambiguity resolution and belief mismatch handling. The system successfully applies its generalization capabilities to process multiple types of inputs such as mobile sensor readings and brain scans and translation across multiple languages. Medical performance enhances when CNNs work together with alignment-based techniques and cognitive behavioral approaches. The data learning process operated at a pace of 8.25 s using 50 training rounds to reach a training success rate of 75%. The detection accuracy along with hallucination mitigation make progress in high-quality datasets according to precision and F1-score metrics despite enduring difficulties in low-resource contexts. The performance assessment consists of accuracy/loss trend analysis together with confusion matrix evaluations and ROC-AUC curve measurements. Extensive dataset optimization and expansion work as key factors to boost CNN-based systems when they operate in real-world scenarios.