COVID-19 is considered a severe viral disease caused by the novel SARS-CoV-2, which emerged in Wuhan, China, in late 2019 and rapidly escalated into a global pandemic, prompting a significant shift in our approach to interacting with one another. Consequently, this pandemic has led to global disruption and the closure of businesses, sports activities, and schools, prompting all institutions to transition to online platforms due to the government’s obligation to implement the Movement Control Order (MCO). The outbreak of COVID-19 has brought unprecedented closures of university facilities, affecting millions of students worldwide. In this project, four classifiers are being implemented: Multiclass Neural Network, Multiclass Logistic Regression, Multiclass Decision Jungle, and Multiclass Decision Forest. The primary objective of this study is to develop an effective classification model for assessing the impact of the learning process under conditions of distance learning during the COVID-19 pandemic. The study aims to i) implement four different classifiers independently on the Distance Learning Effectiveness utilizing the COVID-19 dataset; ii) determine the best classification model among the four. The evaluation process is conducted based on accuracy, precision, and recall metrics. In comparison to the other three models, the results showed that Multiclass Decision Jungle performs better across all evaluation metrics. As long as the dataset’s properties remain the same, the results suggest using Multiclass Decision Jungle in the future.