Bladder cancer ranks among the most prevalent cancers globally, with its incidence steadily rising. Early diagnosis and effective treatment hinge on accurate detection methods. This study leverages machine learning techniques to predict bladder cancer using urinary biomarkers, addressing key challenges such as data variability and model scalability. A dataset comprising clinical laboratory results from 1336 patients underwent extensive preprocessing, balancing, and analysis to ensure robustness. The machine learning models employed include Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Feature selection was performed using tree-based methods such as Random Forest and LightGBM to identify key predictive biomarkers, including Creatinine, Alk, AST, Glucose, Calcium, ALT, Albumin, and Sodium. These features, representing critical clinical and biochemical parameters, were used to train individual models. An ensemble learning approach was then applied to integrate these models, significantly enhancing predictive performance. The ensemble method outperformed individual models, achieving a final accuracy of 83.21%, demonstrating its effectiveness in improving bladder cancer detection. While the results indicate strong performance, further research is required to evaluate the approach’s scalability in larger datasets and diverse clinical settings. Additionally, integrating this method into real-world diagnostic workflows and assessing its impact on clinical decision-making would provide more insights into its practical applicability.