Distance learning has expanded rapidly, but in many institutions — especially in emerging digital economies such as Uzbekistan — it rests on a fragmented set of platforms that rarely interoperate, producing data silos, duplicate logins and brittle custom integrations. This article proposes an integrated digital platform ecosystem for distance learning that unites the learning management system (LMS), artificial intelligence (AI) tools and learning analytics, and it formulates a staged roadmap for improving distance learning. The aim of the study is to design a standards- based ecosystem architecture and a maturity roadmap adapted to the national context. The study relied on documentary analysis, architectural and capability mapping, and conceptual synthesis, drawing on interoperability standards, the scholarly literature, and official statistics from the last five years. The analysis shows that the LMS serves as the core of the ecosystem, bound to specialized tools through the 1EdTech family of standards (LTI, xAPI, Caliper and SCORM), and that an interoperability-first strategy converts a quadratic integration burden into a linear one. Above this foundation, a learning-analytics layer turns behavioural data into key performance indicators and early-warning signals, and an AI layer adds adaptive and generative capabilities. A five-stage roadmap — from fragmented tools to an optimized, closed-loop ecosystem — is proposed. Official data confirm the relevance of the model for Uzbekistan, where higher-education enrollment has grown almost fivefold since 2016 and the national HEMIS platform provides a ready core. The novelty lies in integrating the three pillars into a single ecosystem and roadmap. The results are of practical value for institutions, developers and policymakers.