In rapidly evolving business environments, traditional static machine learning models often underperform due to data drift, market uncertainty, and structural changes in organizational behavior. Adaptive Machine Learning (AML) models—capable of learning continuously, adjusting parameters in real time, and accommodating evolving data patterns—offer significant strategic advantages. This research paper synthesizes literature on AML, proposes a Unified Adaptive Learning Framework (UALF), and introduces a Dynamic Adaptation Cycle (DAC) for enterprise implementation. It further explores reinforcement learning, online learning, meta-learning, and concept-drift detection techniques suitable for volatile business conditions. Figures, conceptual models, and practical examples illustrate how adaptive models enable responsiveness in finance, logistics, marketing, supply chain management, and cybersecurity. The study concludes with implications, limitations, and recommendations for future work.