The purpose of this article is to investigate the impact that machine learning has had on the conventional human resource environment, particularly with regard to employee reviews. Machine learning provides a wide range of data analysis skills, which is something that businesses are actively looking for in order to create more effective methods for evaluating their employees. Because of these algorithms, productivity measures, project achievements, and qualitative input are much improved which results in the creation of performance images that is both complete and objective. The researcher investigates the benefits and drawbacks of implementing this digital giant into human resources. For the purpose of improving performance assessments in contemporary businesses, it is essential to strike the ideal balance between human expertise and technological advancements. The age-old practice of subjective evaluations is becoming increasingly out of date in the ever-changing world of the modern workplace, where results are of the utmost importance. In this study, we investigate the impact that machine learning has had on performance evaluations in human resource management (HRM), focusing on its capacity to deliver assessments that are both objective and nuanced. This study investigates the complexities and potential dangers of machine learning in human resource management (HRM), with the primary objective of achieving performance evaluations that are accurate and equitable. The purpose of this research endeavour is to investigate the sophistication of this method while also locating any possible mistakes. For businesses that want to maximise the potential of their employees by incorporating both technological and human expertise into performance evaluations, this paper provides a detailed roadmap that can help them achieve their goals. The performance of a company's employees is a significant factor in determining the company's potential for commercial success and expansion. On the other hand, the evaluation of the staff's’ performance up to this point has been insufficient and downright disappointing. In order to ensure that an employee's performance is evaluated and predicted in a fair manner, this study investigates several external aspects that are related with their lives, including those that are physical and environmental, social, and economical. My research focuses on building an objective algorithmic way for artificial intelligence to forecast future employee performance. This method takes into consideration a variety of environmental parameters, including physical, social, and economic characteristics.