Mental Health is a ubiquitous contemporary issue causing significant mortality and morbidity in today's society. There exists a strong need to develop prompt and robust diagnosis and treatment options. Further, with altered workplace conditions (especially in the Information Technology field) and more individuals working from home, a safe, supportive and flexible work environment is paramount. This is where the real potential of Machine Learning (ML) and Artificial Intelligence (AI) comes into play. The intersection of ML, AI and Mental Health in Personalized Healthcare is a rapidly growing field, opening up a myriad of new opportunities in terms of early diagnosis, prompt interventions and personalized healthcare plans on a patient-specific basis. This research seeks to apply ML to predict the likelihood of an individual developing a Mental Health condition. The work also considers specific mental states such as Anxiety Disorder and Mood Disorder based on a series of variables containing information about their workplace conditions, Mental Health history and hereditary factors. It also seeks to discover the most important contributing factors to the advent of each Mental Health condition. Three predictive models are developed, each with a strong level of performance, particularly for predicting the presence of a General Mental Health Disorder and Mood Disorder. The research finds the employee's ease of leave accessibility, the overall level of importance an employer places on staff Mental Health and the level of company-wide support in addressing Mental Health concerns to be critical factors influencing employees' Mental Health. Thus, this research can support workplace authorities in identifying at-risk cohorts for developing a Mental Health illness. They can suggest specific changes to their work environment to reduce such risk levels based on such identification.