This research aims to enhance the effectiveness of lung cancer classification using Support Vector Machines (SVM) with hyperparameter tuning. The study employs Radial Basis Function (RBF) kernels to handle non-linear problems and uses Random Grid Search to optimize hyperparameters. The best parameter settings found are C = 10, Gamma = 10, and Probability = True. The results show significant improvements in accuracy, precision, specificity, and F1 score, with a slight decrease in recall (0.02). The proposed method achieves an accuracy of 0.99, precision of 1.00, recall of 0.98, F1-score of 0.99, and specificity of 1.00. The research confirms the potential of tuned SVMs in addressing complex data classification challenges and offers valuable insights for medical diagnostic applications. The study also highlights the importance of both recall and specificity in disease classification, especially in imbalanced datasets.This research aims to enhance the effectiveness of lung cancer classification using Support Vector Machines (SVM) with hyperparameter tuning. The study employs Radial Basis Function (RBF) kernels to handle non-linear problems and uses Random Grid Search to optimize hyperparameters. The best parameter settings found are C = 10, Gamma = 10, and Probability = True. The results show significant improvements in accuracy, precision, specificity, and F1 score, with a slight decrease in recall (0.02). The proposed method achieves an accuracy of 0.99, precision of 1.00, recall of 0.98, F1-score of 0.99, and specificity of 1.00. The research confirms the potential of tuned SVMs in addressing complex data classification challenges and offers valuable insights for medical diagnostic applications. The study also highlights the importance of both recall and specificity in disease classification, especially in imbalanced datasets.