These days, association rule mining is a crucial area of study. Both the hybrid dimensional association rule mining and the general survey of multidimensional association rules are included in this study. The various methods for mining multidimensional and hybrid dimensional association rules are demonstrated in this paper. The conditional hybrid dimensional association rule is also explained in this work, which also determines the optimal method for mining the multidimensional and conditional hybrid dimensional association rule. Educational data mining has the potential to use a vast quantity of research to address a variety of learning, cognitive, and assessment-related educational issues. Although student enrollment has significantly increased in the national setting of the school education system, student performance remains inadequate. The Indian government's Ministry of Human Resource Development has released comprehensive data regarding the school system's dropout rates. Before creating a program for pupils to improve their performance, the school education system requires that students' learning behaviors be analyzed. Additionally, early student performance prediction helps management take corrective action to improve student accomplishment. Data mining offers a wide range of methods for analyzing and forecasting student performance. Given the state of the educational system today, a decision support system that relies solely on mining techniques is unable to manage the massive information and provide complicated answers. The goal of this study is to offer a strategy for the educational system in schools to address the intricate educational problems and increase student performance. Data mining technologies have been taken into consideration in this research project in order to address educational questions.