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The COVID-19 outbreak has underscored the significance of Open and Distance Learning (ODL) as a pivotal tool for teaching and learning. As a result, lecturers must adopt new teaching approaches for ODL to ensure that all students gain a comprehensive understanding. Traditional methods like Cronbach alpha, correlation, and regression analysis fall short in handling indeterminate information and few researchers use Fuzzy Relational Maps (FRM) and Neutrosophic Relational Maps (NRM) to analyze the relationships between lecturer and student’s academic performance in ODL. Utilizing FRM and NRM, this study analyzed the relationship between lecturer and student’s academic performance. Due to the scarcity of studies, utilizing FRM and NRM to explore this correlation, this study employs both methodologies. The six-step process, which involves FRM and NRM methods, including a comparative step, is implemented to attain the research objectives. Furthermore, an interview is conducted using a questionnaire with two experts, one lecturer and one student in UiTM Machang Campus, where the results are constructed into a directed graph and relational matrix. After multiplying the vector with the matrix until got the fixed point, it produced different results for both experts with different kinds of cases and the results were compared between FRM and NRM on two cases. As a result, the comparison reveals that FRM provides more specific impact patterns, while NRM offers more detailed representation, considering the uncertainties in capturing the complexities of factors that influence students’ academic performance. In conclusion, this study uses FRM and NRM to comprehensively analyze the relationship between lecturer and student’s academic performance of ODL in UiTM Machang Campus, achieving the aim of understanding complex instructional influences on students’ academic performance.