This study explores the contextual factors influencing Moroccan students’ performance in mathematics, based on data from the TIMSS 2023 cycle. Using machine learning techniques, particularly autoencoders for dimensionality reduction and various classification algorithms, the analysis identifies key predictors of achievement. These include pedagogical practices such as prompting students to explain their reasoning, student engagement with digital tools, and perceptions of school climate. The results are contrasted with those from TIMSS 2019, highlighting ongoing issues such as limited student motivation and persistent resource gaps while also pointing to encouraging developments associated with national initiatives like the GENIE program. These findings underscore the necessity of a comprehensive strategy that combines robust infrastructure, continuous teacher development, and learner-centered pedagogical approaches to improve mathematics outcomes. To support informed educational planning and classroom practices, the study introduces a predictive modeling framework aimed at facilitating data-driven decision-making.