Calculus as a math course is an important subject students need to succeed in, to venture into STEM majors. The paper focuses on the early detection of at-risk students in a calculus course which can provide the proper intervention that might help them succeed. Calculus has high failure rates which corroborate with the data collected from our University that shows us that 40% of the 3266 students whose data were used failed in their calculus course. Some existing studies similar to our paper make use of open-scale data that are lower in data count and perform predictions on low-impact MOOC-based courses. Paper proposes, an automatic detection method of academically at-risk students by using Learning Management Systems (LMS) activity data along with the student information system (SIS) data from our University for the Math course. The proposed method will detect students at risk by employing machine learning to identify key features that contribute to the success of a student. The model developed has a predictive accuracy of 73.5 % on the online modality of the Math course and has 87.8 % accuracy on the face-2-face (F2F) modality of the same class. Transfer student, a binary feature attributed to the highest feature importance.