This study offers a thorough mathematical framework for early disease identification by combining machine learning (ML) with natural language processing (NLP) in a synergistic way. With a thorough mathematical analysis of Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based models, we establish a rigorous theoretical foundation that extends from traditional statistical techniques to contemporary deep learning architectures. In addition to introducing sophisticated feature extraction methods from multimodal healthcare data using spectral graph theory and manifold learning, the research offers new probabilistic formulations for illness progression modelling using stochastic differential equations. Our technique combines topological data analysis for pattern identification in high-dimensional medical data, information-theoretic methods for feature selection, and Bayesian inference for uncertainty quantification. Extensive statistical study of real-world datasets validates the framework, which shows higher prediction ability with AUC-ROC values as high as 0.958. This work offers useful applications for clinical decision support and makes a substantial theoretical contribution to the mathematical modelling of healthcare AI systems.