The high mortality rate associated with lung cancer worldwide emphasizes the importance of developing better risk prediction tools and personalized treatments. This paper presents a chronotherapy-aware multimodal deep learning framework that integrates histopathological imaging and clinical patient data for lung cancer risk prediction. A CNN-based model extracts discriminative image features, while an MLP processes structured clinical attributes, with class imbalance handled using SMOTE and class weighting. The learned representations are fused into a joint embedding space to estimate patient-level cancer risk probabilities. The results obtained from the experiments demonstrate that the multimodal fusion model outperforms unimodal approaches, achieving an accuracy of 89%, a macro-averaged F1-score of 0.65, and ROC-AUC of 0.81, compared to 34% accuracy for the image-only model and 69% accuracy for the clinical-only model. Additionally, a simulation-based chronotherapy-aware module models circadian rhythm-driven treatment efficacy using predicted risk scores, enabling personalized treatment-timing recommendations. The proposed framework demonstrates the effectiveness of multimodal learning combined with temporal treatment awareness for next-generation clinical decision-support systems.