<p>Elective surgery scheduling not only affects the efficiency of key medical resources but also affects the life and health of patients. The uncertainties, such as surgery duration and the length of stay, pose significant challenges to elective surgery scheduling. However, the current scheduling methods for elective surgeries still cannot cope with this high level of uncertainty, as they cannot fully utilize existing data, surgeon opinion, and other multidimensional information. Therefore, we propose an intelligent scheduling model for elective surgery based on machine learning and expert inference. Specifically, a multi-source information integration algorithm based on machine learning technology and fuzzy set theory is proposed to predict uncertain parameters. Then, we establish a scheduling model for elective surgery based on fuzzy optimization technology. Finally, an algorithm based on the Benders-dual cutting plane is proposed to solve the model. Through actual hospital data, it was proven that the proposed method could effectively improve the predictive accuracy by 38.1% for uncertain parameters and the efficiency of the elective surgery schedule model.</p>