Personalized learning is an innovative pedagogical approach aimed at addressing the specific needs of learners. This article explores the different types of personalized learning such as adaptive, differentiated, competency-based, AI-assisted, or data-driven learning. By relying on an analysis of 28 articles from the Scopus database, we analyze how these types of learning are addressed in the literature and identify the most commonly used methodological approaches. The results show significant disparities in the coverage of personalized learning types, with some being extensively studied, such as adaptive learning and AI-based learning, while others, such as interest-based learning or personalized socio-emotional learning, remain underexplored. Seven major categories of methods have been identified: the development of personalized frameworks and systems, quantitative and analytical approaches, mixed methods, qualitative methods, literature reviews and technological analyses, action research and co-design, as well as technological innovations and the use of artificial intelligence. Furthermore, the most commonly used methodologies include quantitative empirical studies based on learning analytics dashboards and experimental assessments of intelligent learning systems. Based on these observations, we propose to focus future research on the less studied types of personalized learning, applying the most promising methodologies identified in this analysis, in order to address existing gaps and enrich the field of personalized learning.