Oral Squamous Cell Carcinoma (OSCC) is one of the most common and aggressive malignancies in the oral cavity, often diagnosed at advanced stages, resulting in poor prognosis and high mortality rates. Recently, Deep Learning (DL), a subset of Artificial Intelligence (AI), has shown remarkable capabilities in analyzing complex medical data, particularly in medical imaging. This paper investigates the potential of DL models for predicting OSCC using histopathological images, radiological data, and clinical information. Convolutional Neural Networks (CNNs) have been widely employed to extract critical features from tissue images and have demonstrated promising performance in tumor classification, staging, and prognosis prediction. Despite these advancements, challenges such as the requirement for large, annotated datasets, overfitting risk, and lack of model interpretability remain significant. Integrating DL with domain expertise, leveraging transfer learning techniques, and developing hybrid models may pave the way for more accurate and early detection of OSCC, ultimately improving patient outcomes. This study introduces a DL based framework for the prediction of OSCC using histopathological images, radiological scans, and clinical information. The proposed CNN–BiLSTM architecture with an integrated attention mechanism captures both spatial and sequential dependencies across multimodal datasets. Unlike previous studies that rely solely on single-modality data, our method introduces a novel integration strategy that improves predictive accuracy and interpretability. Experimental results demonstrate that the framework achieves 95.7% accuracy, 94.3% sensitivity, and 96.5% specificity, significantly outperforming conventional baselines. These findings highlight the originality of our approach and its potential contribution to advancing real-world diagnostic support systems.