The evaluation of site quality for mixed forests is a comprehensive approach to analyzing forest site conditions and tree species growth performance. Accurate site quality assessment is crucial for understanding and enhancing the ecological functions and productivity potential of forests. This study focuses on mixed forests in Lishui City, Zhejiang Province. Using the Two-way Indicator Species Analysis (TWINSPAN) method, coniferous mixed forest, broadleaved mixed forest, and mixed coniferous–broadleaved forests in the region were classified into 15 forest types. Site form models for each type were then constructed using the Algebraic Difference Approach (ADA) to categorize site quality levels. Subsequently, a site quality classification model was developed by integrating site and climatic factors, employing four machine learning algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and XGBoost. This model effectively facilitated the evaluation of site quality in mixed forests. The results showed that, across the 15 forest types, the site form models based on the ADA method achieved R² values greater than 0.634, indicating accuracy in capturing tree height growth trends in mixed forests. For site quality classification, all four models (RF, KNN, SVM, and XGBoost) achieved overall accuracies above 0.77. Among these, the machine learning models ranked in effectiveness for site quality classification as follows: XGBoost > RF > SVM > KNN. These findings suggest that the site form model is a suitable criterion for classifying site quality in mixed forests in Lishui City, Zhejiang Province, and that the XGBoost-based model demonstrates strong classification accuracy. This study provides a scientific basis for site-adapted tree selection and advances information on mixed forest management.