Cloud migration has evolved from a manual, rule-based transition to an intelligent, data-driven evolution. As organizations face the complexities of hybrid and multi-cloud environments, traditional \"lift-and-shift\" methods often result in unforeseen costs and performance bottlenecks. This review explores the integration of Machine Learning (ML) as a pivotal force in streamlining cloud transitions. By leveraging predictive analytics, pattern recognition, and automated decision-making, ML-driven strategies enable precise workload discovery, cost optimization, and proactive risk mitigation. We examine how algorithms—ranging from supervised learning for resource forecasting to unsupervised clustering for application dependency mapping—can drastically reduce the \"cloud sprawl\" that plagues modern enterprises. This article synthesizes current methodologies, highlighting the shift from static migration planning to dynamic, self-optimizing cloud ecosystems. Ultimately, the synthesis of ML and cloud strategy ensures that the digital transformation journey is not just a change in infrastructure, but a measurable improvement in operational agility and fiscal responsibility.