Lung cancer is one of the most common and deadly cancers worldwide, with low survival rates due to delayed identification. Timely and accurate diagnosis can improve treatment outcomes and reduce mortality. Histopathological imaging remains the gold standard for diagnosis, but manual inspection is laborious and subjective. In this context, deep learning approaches can automate classification and support pathologists in making reliable decisions. This research presents a deep learning-based multi-class lung cancer classification framework using histopathological images. The dataset consists of three categories: benign, adenocarcinoma, and squamous cell carcinoma. Multiple CNN architectures, including MobileNetV2, ResNet-101, and EfficientNet-B3, were evaluated. Among them, MobileNetV2 achieved the highest classification accuracy of 99.1%, while EfficientNet-B3 achieved 97.2% with a strong balance across precision, recall, F1-score, and ROC-AUC. To address class imbalance and overfitting, transfer learning, class weighting, and data augmentation were applied. Confusion matrix analysis further confirmed the ability of the models to distinguish between similar cancer subtypes. The findings highlight the potential of EfficientNet-based CNNs for reliable medical image analysis and digital pathology. Future work will explore Vision Transformers and attention mechanisms to enhance diagnostic accuracy and interpretability. Overall, this research demonstrates the promise of AI-supported histological evaluation and its role in advancing clinical decision support.