Objective To develop a postoperative pain intelligent assessment system based on multimodal physiological data and artificial intelligence (AI) algorithms, to evaluate its effectiveness, and to provide an intelligent solution for postoperative pain management. Methods Heart rate, systolic blood pressure, diastolic blood pressure, electrodermal activity, respiratory rate, and facial expression features were used as pain assessment indicators.A multi-algorithm fusion AI assessment model was developed and validated using random forest （RF）, support vector machine （SVM）, and convolutional neural network （CNN） algorithms.A total of 800 postoperative patients treated between January and June 2025 were assigned to the control group and received conventional pain nursing care.Another 800 postoperative patients treated between July and December 2025 were assigned to the observation group, in which the intelligent assessment system was applied for pain assessment and early warning.The accuracy of pain assessment within postoperative 24 h and patient satisfaction with pain management were compared between the two groups. Results The accuracy rates for assessing mild, moderate, and severe pain in the observation group, as well as patient satisfaction score for pain management, were significantly higher than those in the control group (all P＜0.05). Conclusion The postoperative pain intelligent assessment system based on multimodal physiological data and AI algorithms can enhance the accuracy of pain assessment and patient satisfaction with nursing care.