The integration of AI and ML with genomics has been pushing personalized medicine ahead by better predicting disease risk using the gene expression data. However, such data is high-dimensional and complex in nature, creating huge difficulty. In this study, the problem is handled by predicting disease risk from gene expression data using Random Forest (RF), Decision Tree, and Logistic Regression models. Before fitting the model, we concluded numerous preprocessing techniques, i.e., algebraic normalization and variance thresholds to guarantee high-quality data. RF demonstrated the best performance between models, with good accuracy, above all others compared to Decision Tree and Logistic Regression. In comparison, the tree-structured like single-tree of Decision Tree made it less generalized and Logistic Regression’s linearity was inadequate to capture possible nonlinearity information within gene expression patterns. The results suggested that the RF model had good scalability and accurate prediction ability for disease occurrence, which is of great significance to make it a helpful tool in clinical applications. This study demonstrates the benefits of ensemble learning approaches in genomics and suggests that Random Forest is able to enhance precision medicine by improving disease prediction, variably but with a positive trend leading toward personalized perfect accuracy.