There is an immediate need for more accurate and reliable methods in prostate cancer detection to improve patient results through fewer unnecessary interventions. The methods used currently mostly rely on single-modal data, hence this leads to limited performance because the disease is complex and heterogeneous. This paper addresses these by coming up with a new framework for prostate cancer detection based on Multimodal Fusion Technique, GANs, and Uncertainty Estimation. The proposed multilateral fusion technique, Multi-Modal Variational Autoencoder, mainly integrates multi-modal information about the knowledge encoding MRI, PET scans, CTs, and histopathological images. This approach captures complex relationships between modalities within a probabilistic framework; thus, it increases sensitivity and specificity, and both metrics show a significant increase over single-modal approaches Generative Adversarial Networks particularly conditional GANs are engaged for synthesizing images in various imaging modes; thereby, broadening the scope of training data and enhancing model robustness with respect to changes in data. The proposed framework seems promising, as preliminary results show improved sensitivity, specificity, and even decreased classification error rates. Combining modality-based approaches with synthetic data generation and uncertainty quantification will revolutionize prostate cancer detection because it will give clinicians more reliable diagnostic tools. This work has primarily resulted in the maximization of clinical practice and improvement in the accuracy of diagnostic value, minimization of unwanted interventions, and better outcomes for patients with prostate cancer management.