Oral cancer and in particular oral squamous cell carcinoma are one of the major public health burdens worldwide with late stage diagnosis associated with poor survival rates. This research paper provides a detailed study on next-generation multimodal artificial intelligence (AI) frameworks that have been developed to implement early oral cancer prediction and individual-specific treatment planning. The work investigates the fusion of different data types such as histopathological images, clinical metadata, genomic profiles and radiological imaging using the use of groundbreaking deep learning architectures such as convolutional neural networks, transformer models and ensemble learning methods. We propose a unified multimodal AI-Clinical Decision Support System (AI-CDSS) which can act as a synthesis of information from multiple sources with unprecedented diagnostic. The framework includes explainable AI (XAI) methods such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) to make the clinical interpretation and level of trust more. Additionally, personalised treatment recommendations based on unique patient characteristics are made possible by precision medicine approaches based on genomic profiling and molecular biomarkers. For cutting- edge models, performance evaluation in the form of receiver operating characteristic (ROC) curves displays values of area under the curve (AUC) integrating greater than 0.97. By offering non-invasive AI-assisted triage tools to reduce diagnosis delays and support clinicians in making decisions, this research is filling important gaps in current diagnostic workflows. This can ultimately improve patient outcomes through prompt intervention and targeted therapy.