Lung cancer is a major global health threat, affecting millions annually and resulting in severe complications and high mortality rates, particularly when diagnosed late. It remains one of the leading causes of cancer-related deaths worldwide, often detected at advanced stages due to the lack of early symptoms. This study introduces a novel hybrid machine learning model aimed at enhancing early detection accuracy and improving patient outcomes. By integrating traditional machine learning classifiers with deep learning techniques, the proposed framework optimizes feature selection, hyperparameter tuning, and data-balancing strategies, such as Adaptive Synthetic Sampling (ADASYN). A comparative evaluation with existing models demonstrated substantial improvements in predictive accuracy, ranging from 0.44% to 9.69%, with Gradient Boosting and Random Forest models achieving the highest classification performance. The study highlights the importance of hybrid methodologies in refining lung cancer diagnostics, ensuring robust, scalable, and clinically viable predictive models.