This study employs the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to develop a decision support system for selecting the best online learning platform. Given the proliferation of diverse online learning platforms, selecting the most appropriate one poses significant challenges due to varying functionalities such as usability, cost, accessibility, technical support, and content quality. By integrating these criteria into the TOPSIS framework, the study quantitatively evaluates and ranks multiple platforms to identify the most suitable one based on their closeness to an ideal solution. The analysis revealed that Platform E is the most aligned with the ideal characteristics, excelling across multiple criteria, followed by Platform C and B, with distinct strengths in accessibility and cost-effectiveness, respectively. The study highlights the advantages of using a multi-criteria decision-making approach in complex decision environments, emphasizing TOPSIS’s capability to handle both qualitative and quantitative data effectively. While acknowledging the influence of subjective weight assignments and criterion interdependence, the research suggests integrating other decision-making models for robustness. This approach not only streamlines the selection process but also enhances the decision-making quality, aiding stakeholders in navigating the complex digital educational landscape. The findings advocate for ongoing updates and adaptations in criteria to reflect changing technological and user needs.