Although Macau students have consistently been recognized as top performers in international assessments, little research has been conducted to explore the various factors that are associated with their achievement. This paper aimed to identify factors that could best predict Macau students’ reading achievement using PISA 2018 data provided by 2,979 15-year-old students. An integrative theoretical model that considered the critical roles of demographic, personal, and social-contextual factors was used to understand the relative importance of 41 different factors in predicting reading achievement. A machine learning approach, specifically Random Forest Algorithm, was used to analyze the data. Results indicated that variables classified under personal factors (e.g., metacognitive strategies, reading enjoyment, and perceived difficulty) were the most important predictors of Macau students’ achievement. A supplementary analysis using Hierarchical Linear Modelling confirmed the findings from the machine learning approach. Implications of the findings were discussed.