<p>Heart disease, diabetes, hepatitis, and breast cancer are among the leading causes of global mortality, emphasizing the urgent need for advanced computational models to support early diagnosis and effective treatment planning. This study introduces the Deep Type-2 Fuzzy Cerebellum Model Artificial Predictor (DT2FCMAP), a novel predictive framework that integrates deep fuzzy logic-based learning with hierarchical inference mechanisms to enhance disease classification accuracy while effectively managing uncertainty in clinical data. DT2FCMAP employs a multi-layered Type-2 fuzzy cerebellar structure, enabling it to model complex disease interactions and improve prediction robustness. The proposed method is validated on multiple medical datasets, achieving over 99% accuracy, significantly outperforming conventional machine learning and deep learning models such as LSTM and XGBoost. Its ability to handle noisy, imbalanced, and uncertain medical data makes it particularly reliable for real-world disease diagnosis. Beyond medical applications, the generalized structure of DT2FCMAP allows for its extension to other predictive modeling tasks, such as risk assessment and anomaly detection in complex datasets. These results confirm that DT2FCMAP is a highly interpretable, scalable, and efficient computational model for disease diagnosis and other applications.</p>