This study introduces a groundbreaking hybrid deep learning method aimed at early detection of lung cancer, leveraging neural networks. The proposed approach combines the strengths of convolutional neural networks (CNNs) for image feature extraction and recurrent neural networks (RNNs) for temporal analysis of patient data. By integrating these networks into a cohesive framework, the model effectively learns complex patterns and temporal dependencies from diverse data sources, including medical images and patient records. Furthermore, the system employs attention mechanisms to focus on relevant features and enhance predictive accuracy. Experimental results demonstrate superior performance in early lung cancer detection compared to traditional methods, underscoring the potential of this hybrid deep learning approach for improving diagnostic outcomes and patient care.