This study provides insight into how machine learning methods, in particular k-means clustering algorithm could contribute to greater degree of employee engagement in the businesses. Using Work-Life Balance, Environment Satisfaction and Job Satisfaction found in employee survey data as an illustrative lens of the engagement phenomenon, patterns are identified that differ from traditional perspectives with implications for organizational actions. The study categorizes workers in clusters and identifies the significant gaps of satisfaction among them, using k-means clustering. Logistic regression analysis is used for the prediction of attrition risk, which also helps in determining factors responsible behind employee retention. The findings reveal the importance of understanding such facilitators to generate targeted interventions and strategies that foster a positive work environment and improve organisational performance. This approach ensures less attrition risks, and better job satisfaction leading to greater overall organisation productivity / wellbeing.