The executive level employee selection process is emerging as a major challenge for the HR department. It is of prime importance for recruiters to select employees based on their competencies and competencies keeping in view the human resource requirements of the organization. Selection of employees is always based on various parameters related to knowledge and skills. This research explores the application of machine learning techniques to assess executive level skills with the aim of enhancing leadership performance. In general, traditional methods of skill assessment often suffer from subjectivity and limited scalability. This study provides a more objective, data-driven approach to executive level skill assessment using machine learning techniques. Using machine learning algorithms, we have developed models to predict key executive level skills such as critical thinking and creativity skills, corporate exploration skills and decision making skills. The results demonstrate that machine learning models can effectively predict executive level skill parameters with higher accuracy than traditional assessment methods, outperforming them. Furthermore, the study highlights the importance of feature selection, demonstrating that certain performance indicators and behavioral traits are strong predictors of the effectiveness of executive level skills. Our findings suggest that incorporating machine learning into the skills assessment process can provide more reliable and actionable information, aiding in the development of tailored training programs and better-informed executive level promotions. This research contributes to the growing field of data-driven human resource management and provides a practical approach for organizations seeking to enhance their leadership capabilities. Future research will focus on refining these models and exploring their application in different cultural and organizational specific need-based contexts.