Much of the existing educational research has focused on affluent Western societies. Despite comprising a broad swathe of the world population, less work has focused on lower middle-income economies such as the Philippines. Perhaps part of this reason is the lack of high-quality data in such contexts. PISA provides a potential solution to this problem. The Philippines participated in PISA in 2018 for the first time and scored lowest in reading out of 78 participating economies. This study was rooted in the bioecological model and simultaneously considered the roles of personal characteristics, proximal processes, and contextual factors to identify the most important predictors of reading achievement in the Philippine context. We used the 2018 PISA Philippine data, which consisted of 7233 15-year-old adolescent participants. Machine learning was used to select important variables. Results revealed the 26 top predictors of reading achievement out of 52 variables. The most critical predictors in order of importance included reading difficulty self-concept, socioeconomic status, grade repetition, school belonging, and fixed mindset among others. Hierarchical linear modelling broadly indicated similar results with small to moderate effect sizes. Theoretical and practical implications were discussed.