The demand for in-depth studies on educational data presupposes the application of technologies that allow data analysis of vast quantities, and subsequently, drawing relevant information and knowledge. The research objective herein is to employ data mining techniques on PISA databases to identify potential patterns that may explain the top-performing countries’ success. Accounting for the methodology, data acquisition, bank creation, and countries’ data extraction, we ran preprocessing and data cleaning and mining stages, respectively; in the last phase, we used the J48 method for classification purposes. From the decision trees, the study identified the relevant attributes which relate to student educational level aspiration; failure; motivation and anxiety; socioeconomic factors; scientific approaches; the use of information and communication technologies; interactions with friends; physical activity practice; paid work; home assignments; learning time for each discipline; cooperation and teamwork; the student’s study program; the teacher’s fairness; and the school year in which the student is enrolled. In this regard, results were considered satisfactory for allowing the analyses of these aforementioned relevant attributes associated with PISA best-ranked countries.