Early and accurate cancer identification remains one of the most significant problems in clinical data analysis. In this paper, a hybrid classification model that integrates Random Forest (RF) and Support Vector Machine (SVM) models is presented and evaluated to enhance diagnosis accuracy based on available cancer datasets from Kaggle. The method employs a hybrid soft-voting ensemble (RF + SVM) over the standalone models to measure predictability performance and gains in robustness. Feature scaling, stratified data split, and probabilistic voting were applied in order to ensure balanced learning and generalization. The comparative evaluation demonstrates that the hybrid model outperformed both base classifiers with overall accuracy 0.972, precision 1.000, recall 0.925, and F1-score 0.961, along with AUC 0.998. Performance visualization by ROC, Precision–Recall, and calibration plots repeated the hybrid model's superior discriminative performance and calibration robustness. Threshold sweep analysis continued to further improve classification sensitivity for identifying malignancies. The results affirm that tree-based ensemble learning and kernel-based separation integration enhances classification robustness in medical diagnosis tasks. The hybrid solution is applicable as a likely foundation for next-generation intelligent diagnostic systems integrating multiple learning paradigms in handling complex biomedical data.