The detection and treatment of cancer continue to be major public health concerns worldwide, necessitating novel approaches. Because of its prevalence, a multidisciplinary approach (MA) is required to treat cancer effectively. When compared to an integrated strategy, traditional methods frequently fall short in terms of precision and holistic knowledge. The integration of several medical specialties poses difficulties in terms of organization, technology, and teamwork. Crucial challenges include harmonizing diagnostic and treatment techniques, sharing data, and communicating across disciplines. This research proposes an artificial intelligence (AI) based Multimodal Molecular Imaging Fusion (AI-MMIF) framework for integrating oncology, radiology, nuclear medicine, and imaging. For the purpose of to deliver a patient-centred, data-driven approach, this method makes use of state-of-the-art technologies including artificial intelligence, state-of-the-art imaging modalities, and genomic profiling. There is tremendous potential in the integrated approach to cancer treatment in several areas. It improves the likelihood of a positive outcome by allowing for earlier diagnosis with the help of state-of-the-art imaging techniques, aiding in the localisation and characterization of tumors, and allowing for tailor-made treatment plans. It additionally allows for continuous tracking of both positive and negative outcomes during therapy. Validation and improvement of this holistic strategy are greatly aided by simulation. Performance, effectiveness, and effect on patient outcomes are evaluated using computer modeling and real-world data analysis. Our simulation findings reveal huge opportunities for improving cancer care.