Lung cancer is a major public health concern, and accurate prediction models are essential for early detection and effective treatment. However, existing prediction models often suffer from low accuracy and robustness, especially in the presence of outliers and overfitting. This research study proposes a novel approach for lung cancer prediction that integrates variational autoencoders (VAEs), early stopping, neural network clustering, and optimal tuning. The proposed model was evaluated on a dataset of 10,000 lung cancer patients. The model achieved an accuracy of 92.1% on the test set, which is a significant improvement over the accuracy of previous models. The proposed model also showed improved robustness to outliers and overfitting. The main contributions of this work include the development of a new method for predicting lung cancer that combines various techniques such as Variational Autoencoders (VAEs), early stopping, neural network clustering, and optimal tuning. The proposed approach achieves higher accuracy compared to previous models and demonstrates improved robustness to potential issues like outliers and overfitting. This study represents a significant advancement in the field of lung cancer prediction. The proposed model offers the potential to revolutionize early detection and treatment strategies, leading to improved patient care and outcomes. Additionally, it serves as a foundation for future research in lung cancer diagnostics and management, paving the way for further breakthroughs in this crucial area. This study paves the way for personalized medicine approaches in lung cancer treatment by enabling more precise patient stratification and targeted therapies. The proposed model's potential for early detection translates to significant cost savings for healthcare systems and improved quality of life for patients.