The pandemic COVID-19 outbreak has brought attention to the difficult working conditions faced by healthcare professionals. Due of its substantial impact on individuals’ quality of life, it is imperative to recognize these difficulties and develop ways for efficiently managing the mental and emotional stress that comes with them. Many elements, including the quantity of professionals available for every 100 patients, resource management by the government comes into play in a diversified nation like India with a large population. Healthcare personnel are affected by the shortcomings of the above elements. Utilizing machine learning techniques, this study seeks to identify and anticipate stressors experienced during COVID-19 by healthcare professionals. Various techniques are employed to evaluate depression, anxiety, and stress, respectively. In total, 211 responses were considered for the study. Using Python, exploratory data analysis is performed. The sociodemographic profile of healthcare workers is utilized as input for a total of five machine learning algorithms (K-nearest neighbors, gradient boosting algorithm, decision tree, random forest, and support vector machine) to automatically classify and forecast levels of stress, anxiety, and depression. The best performing algorithm is determined. The results will help in identifying the impact of pandemic on healthcare worker and future stress predictions.