<p>In today’s digital age, it has become imperative to unravel the intricate dynamics shaping the digital learning landscape and to comprehend the motivations driving learners toward online platforms. A modified version of total interpretive structural modeling (M-TISM) is used in this study as it enables systematic modeling of complex interrelationships among multiple decision factors and provides hierarchical insights, making it particularly suitable for analyzing platform selection dynamics. The study offers a structured understanding of the interrelationships among platform selection factors, thereby contributing novel insights into the decision dynamics of learners in digital upskilling environments. After analyzing various elements, and based on expert validation (<i>n</i> = 28), we identified ten important variables: course variety, pricing, certification, networking opportunities, career support, community interaction, instructor quality, interactive learning, course quality, and self-paced learning, which are essential for finding the interrelationship between factors a learner considers while selecting e-learning platforms. The findings reveal the key driving factors include price, self-paced learning, certification, and course variety, which collectively shape accessibility, flexibility, credibility, and relevance of learning opportunities. The study contributes by offering a hierarchical interpretation of factors guiding platform selection and provides actionable insights for platform developers, educators, and policymakers to design learner-centric upskilling strategies, thereby highlighting the study’s significance in improving the effectiveness and strategic design of digital upskilling platforms.</p>