This systematic review critically examines the research trajectory and pedagogical significance of Reverse Engineering Pedagogy (REP) within the rapidly evolving landscape of Generative Artificial Intelligence (GAI). The study synthesizes existing literature to delineate the conceptual underpinnings of REP, chart its current implementation contexts, and analyze its technological integration, thereby providing theoretical support and practical guidance for educational practice. Findings indicate that REP, originating in engineering disciplines, has progressively permeated K-12 education, crystallizing into two principal instructional models: restoration and transfer. Evidence substantiates REP’s considerable efficacy in cultivating students’ creative capacities, enhancing self-efficacy, and developing practical competencies. The emergence of GAI presents novel opportunities to transform REP implementation by facilitating the rapid generation of artifacts and enabling personalized feedback mechanisms. GAI can thus mitigate key REP challenges, such as high resource demands and strong reliance on instructor expertise. However, integrating GAI with REP also introduces new complexities, chiefly the adaptation of educator and learner roles and addressing the limitations of current GAI systems. Future research should prioritize expanding GAI capabilities for REP, optimizing GAI-integrated instructional designs, and conducting further empirical studies. This research is vital to unlock the full potential of GAI-enhanced REP, thereby fostering pedagogical innovation and educational advancement.