The migration of large-scale legacy monolithic systems to microservice architecture remains one of the pressing challenges in modern software engineering. This article examines methodological approaches to decomposing a Python-based monolith into an ecosystem of independently deployed microservices while preserving backward compatibility and ensuring controlled, non-disruptive data migration. The study is based on systematic analysis of peer-reviewed academic literature, industrial case studies, and the practical project experience of the author, including the transformation of a Python backend system that resulted in test coverage growth from 27% to 95% and database update acceleration by a factor of eighteen. The research identifies the Strangler Fig pattern combined with Domain-Driven Design (DDD) as the most evidence-supported decomposition strategy, proposes a six-stage migration roadmap incorporating dual-write data synchronization and API versioning, and formulates a risk matrix specific to Python ecosystems. The article contributes an original migration methodology that bridges the existing gap between theoretical decomposition frameworks and practical Python toolchain implementation. The results are of interest to software architects, backend engineers, and engineering managers engaged in system modernization projects.