Postoperative pulmonary complications (PPCs) risk prediction models can help healthcare professionals identify the probability of PPCs occurring in patients after surgery and provide a foundation for rapid decision-making by clinical healthcare professionals. This study evaluated PPCs of lung cancer models' merits, limitations, and challenges, covering construction methods, model performance, and clinical applications. The current risk prediction models for PPCs after lung cancer surgery have a certain predictive effect on the occurrence of PPCs. However, deficiencies persist in study design, clinical implementation, and reporting transparency. Future research should prioritize large-sample, prospective, multi-center studies for multiomics models, ensuring robust data for precise predictions, thereby facilitating clinical translation, adoption, and promotion.