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Counselling students remains a pre-eminence for most tertiary institutions in Ghana to the extent that institutions now have counselling units that extend to the departmental level. This study used the K-prototype machine learning algorithm to cluster students’ behaviour based on 28 relevant attributes and further proposed a classification model. The analysis of the experimental outcomes using the elbow method reveals the formation of three distinct clusters with decreasing intra-cluster similarities and increasing inter-cluster distances. The first cluster uniquely consists of active learners with three or more roommates, primarily in the first year. The second cluster with the highest membership consists mainly of second-year students who exhibit passive classroom conduct and reside in a two-occupancy hostel. The third cluster contains a mixture of third and final-year students who are highly passive in class and live in a tenancy occupancy of two. After clustering, the K-nearest neighbours, logistic regression, naïve Bayes (NB), and AdaBoost ensemble algorithms were implemented to create a model for future learner cluster prediction. Simulation results using the tenfold cross-validation technique show that AdaBoost (NB) has the highest accuracy of 99.88% with an F-measure score of 0.999 and receiver operating characteristic–area under the curve value of 1.00.