The global health crisis triggered by the COVID-19 pandemic profoundly disrupted organizations, forcing them to rapidly adapt to unprecedented challenges. This paper investigates the impact of change management on human resources management (HRM) during crises and demonstrates how Machine Learning (ML) techniques can provide predictive insights into workforce responses to organizational change. Grounded in theoretical frameworks such as Kotter’s change model, Lewin’s change theory, and organizational resilience theory, the study integrates ML-based analysis to identify key factors influencing employee engagement, resistance to change, and organizational adaptation. Using the case of CEGOS, a leading international consultancy and training firm, the paper highlights how strategic HR interventions, supported by predictive analytics, mitigated the effects of the crisis and strengthened organizational resilience. The findings suggest that combining structured change management models with data-driven approaches enables organizations to anticipate employee needs, optimize communication strategies, and enhance adaptive capacity. The paper concludes by discussing implications for strategic HR practices and outlining future directions for integrating ML into change management frameworks in crisis contexts.ML into change management frameworks in crisis contexts. Recent advancements in machine learning and intelligent systems further reinforce the relevance of predictive analytics in HRM during crises, offering enhanced accuracy and adaptability in complex decision-making environments.