Every firm, regardless of its location, sector, or size, is susceptible to the issue of employee turnover, which is a problem that affects all enterprises. Accurately estimating employee turnover is one of the most important objectives of Human Resources (HR) in many firms. This is because it is a significant concern for an organization. Many firms are confronted with the challenge of employee turnover, which occurs daily and results in the departure of valued and experienced workers from the organization. A great number of companies all around the world are working toward the elimination of this significant problem. The primary purpose of this study endeavour is to develop a model that can assist in determining whether or not an employee will quit the organization. Many businesses incur considerable expenditures due to employee turnover, which is a key problem that leads these businesses to spend considerable costs. Providing human resources departments with a beneficial decision support system and, consequently, preventing a significant amount of time and resources from being wasted may be accomplished by utilizing machine learning and artificial intelligence techniques to predict the possibility of an employee resigning and the reasons for their departure. This study aims to present a preliminary exploratory examination of the application of machine learning approaches for predicting employee turnover. Organizations are confronted with enormous expenditures as a consequence of staff turnover. Implementing the Random Forest Classifier method will be utilized to make a prediction regarding employee turnover, which is now feasible thanks to developments in machine learning and data science. Human resources are one of the most valuable assets that an organization possesses. The success of any company or organization is contingent on the people working for that company or organization accomplishing their goals, meeting their deadlines, preserving quality, and ensuring that their customers are satisfied. Employee turnover, often known as employee attrition, is one of the most significant challenges businesses must face in a highly competitive environment. Attrition of employees is a predictable phenomenon under stable conditions, in which a predetermined pattern can be inferred from specific characteristics that influence both the individual and the business at all times. Some of these characteristics may be foreseen, such as the age at which one may retire. In contrast, others may be unforeseeable, such as the success of the firm, other sources of money, management shakeups, and so on. To lower the employee turnover rate, it is vital to evaluate the efficiency of employee evaluations and the degree to which workers are satisfied with their jobs within the organization. Within the scope of this study, a novel strategy that centres on machine learning was utilized to improve various retention strategies for specifically targeted employees. This report also tries to shed some insight into the many elements that influence the attrition rate of workers and the potential remedies to these problems. Many firms are confronted with the challenge of employee turnover, which occurs daily and results in the departure of valued and experienced workers from the organization. A great number of companies all around the world are working toward the elimination of this significant problem. The primary purpose of this study endeavour is to develop a model that can assist in determining whether or not an employee will quit the organization. Having competent personnel is a rare commodity when it comes to having a great company. An issue that poses a danger to business owners is the difficulty of retaining skilled workers with years of expertise. Since it requires much money to reward employees for their experience and efficiency, the problem of employee turnover may be quite expensive for companies. Because of this, the purpose of this research is to propose an automated model capable of predicting employee turnover based on various predictive analytical methodologies. Various pipeline topologies have utilized these methodologies to determine which champion model is the most effective. To recognize valuable employees leaving the firm, implementing this concept will assist management in employee evaluation and decision-making. We aim to create a general attrition prediction platform independent of the application domain and founded on the features of bipartite graphs and machine learning methods. By utilizing this program, it is possible to discover the hidden causes behind the departure of employees, and management can take preventative measures concerning the departure of each employee on an individual basis.