In recent years evidence has emerged suggesting that Mini-basketball training program (MBTP) can be an effective intervention method to improve social communication (SC) impairments and restricted and repetitive beha-viors (RRBs) in preschool children suffering from autism spectrum disorder (ASD). However, there is a considerable degree if interindividual variability concerning these social outcomes and thus not all preschool children with ASD profit from a MBTP intervention to the same extent. In order to make more accurate predictions which preschool children with ASD can benefit from an MBTP intervention or which preschool children with ASD need additional interventions to achieve behavioral improvements, further research is required. This study aimed to investigate which individual factors of preschool children with ASD can predict MBTP intervention outcomes concerning SC impairments and RRBs. Then, test the performance of machine learning models in predicting intervention outcomes based on these factors. Participants were 26 preschool children with ASD who enrolled in a quasi-experiment and received MBTP intervention. Baseline demographic variables (e.g., age, body, mass index [BMI]), indicators of physical fitness (e.g., handgrip strength, balance performance), performance in executive function, severity of ASD symptoms, level of SC impairments, and severity of RRBs were obtained to predict treatment outcomes after MBTP intervention. Machine learning models were established based on support vector machine algorithm were implemented. For comparison, we also employed multiple linear regression models in statistics. Our findings suggest that in preschool children with ASD symptomatic severity (r = 0.712, p < 0.001) and baseline SC impairments (r = 0.713, p < 0.001) are predictors for intervention outcomes of SC impair-ments. Furthermore, BMI (r = −0.430, p = 0.028), symptomatic severity (r = 0.656, p < 0.001), baseline SC impairments (r = 0.504, p = 0.009) and baseline RRBs (r = 0.647, p < 0.001) can predict intervention outcomes of RRBs. Statistical models predicted 59.6% of variance in post-treatment SC impairments (MSE = 0.455, RMSE = 0.675, R = 0.596) and 58.9% of variance in post-treatment RRBs (MSE = 0.464, RMSE = 0.681, R = 0.589). Machine learning models predicted 83% of variance in post-treatment SC impairments (MSE = 0.188, RMSE = 0.434, R = 0.83) and 85.9% of variance in post-treatment RRBs (MSE = 0.051, RMSE = 0.226, R = 0.859), which were better than statistical models. Our findings suggest that baseline characteristics such as symptomatic severity of ASD symptoms and SC impairments are important predictors determining MBTP intervention-induced improvements concerning SC impairments and RBBs. Furthermore, the current study revealed that machine learning models can successfully be applied to predict the MBTP intervention-related outcomes in preschool children with ASD, and performed better than statistical models. Our findings can help to inform which preschool children with ASD are most likely to benefit from an MBTP intervention, and they might provide a reference for the development of personalized intervention programs for preschool children with ASD.