Oral epithelial dysplasia (OED) is a precancerous histopathological finding which remains the gold standard for prediction of malignant transformation to Oral Squamous Cell Carcinoma (OSCC). OED is currently graded subjectively by microscopic examination. The urgent need for objective, biologically informed risk stratification tools has driven the integration of artificial intelligence (AI), spatial transcriptomics, and functional genomics in oral cancer research. This thesis tests the central hypothesis that deep learning and spatial transcriptomic approaches can objectively predict the malignant transformation of OED by identifying histomorphological patterns and immune-epithelial gene signatures associated with malignant transformation zones and cancer progression. To evaluate this, three specific aims were pursued. In Aim 1, we trained and evaluated multiple machine learning and deep learning models, including classical regressors, state-of-the-art neural networks, and weakly supervised pattern-recognition networks using a multi-institutional dataset of annotated H&E whole slide images (WSIs) of OPMD cases with known transformation status. In Aim 2, spatial transcriptomics (Visium HD 10x Genomics) was performed on high-risk oral precancerous samples (proliferative leukoplakia) to identify gene signatures predictive of transformation, with a focus on immune–epithelial interactions and malignant transformation zones. In Aim 3, a 4NQO-induced oral carcinogenesis model was applied to mEAK-7 knockout mice to assess its functional role in OSCC development. In Aim 1, AI models demonstrated that lymphocyte infiltration patterns can predict malignant transformation, with deep learning models achieving accuracies up to 83.4% in distinguishing transformed from non-transformed cases. In Aim 2, spatial transcriptomics revealed downregulation of epithelial barrier genes (FLG, CASP14) and immune activation signatures (S100A8, S100A9, CD74) in malignant transformation zones, supporting a model of barrier disruption and neoantigen-driven immune remodeling. In Aim 3, the mEAK-7 knockout animal study showed significantly reduced OSCC incidence, implicating alternative mTOR signaling in OSCC initiation and validating spatial findings through in vivo functional evidence. In conclusion, this thesis presents an integrated, multi-modal investigation into the malignant transformation of OED, providing evidence that AI and spatial biology can complement conventional pathology in predicting cancer risk. The combined findings offer a foundation for future precision diagnostics in oral cancer prevention and identify novel molecular targets for early intervention.