Within this context, Network Analysis (NA) has emerged as a powerful methodological paradigm, enabling the visualization and quantification of the cognitive structures underlying learning, known as Associative Cognitive Networks (ACNs). This study investigates the impact and application domains of this methodology within science and mathematics education through a mixed-methods systematic review. A systematic synthesis was conducted on 30 studies published between 2014 and 2024, selected in accordance with the PRISMA protocol. The qualitative phase employed thematic coding to identify key application areas, revealing three primary domains: (1) modeling knowledge structures; (2) analyzing dynamic learning processes, with a particular emphasis on Epistemic Network Analysis (ENA); and (3) evaluating educational systems and resources, such as textbooks and curricular designs. The quantitative phase consisted of a random-effects meta-analysis of five quasi-experimental studies, calculating the impact on learning outcomes using Hedges' g. Results yielded a moderate, statistically significant positive effect (g = 0.46, p < .001), indicating that the use of ACN approaches significantly enhances learning outcomes. This study reaffirms the potential of Network Analysis as a transformative tool. By providing a solid empirical basis for understanding learning as the dynamic organization of a cognitive network—rather than a mere accumulation of isolated facts—it renders mental structures visible. In practice, this facilitates conceptual understanding and fosters the development of complex skills such as metacognition and argumentation, aligning pedagogical practices with neurocognitive processes.