COVID-19 has had a worldwide impact. Economically, it has a ripple effect on the entire financial market. COVID has caused significant problems for the majority of insurance companies. COVID had a substantial impact on the life insurance industry. Most people nowadays have both life and health insurance based on how well that insurance company is performing. Because the majority of them purchased insurance, the company’s share capital and risk are both increasing (Nti et al. Artif Intell Rev 53(4):3007–3057, 2020). As the share price of the insurance company fluctuates, insurance uses a hedging concept that minimizes risk while maximizing profit. Because the share price is volatile, it is difficult to forecast. Stock prediction is one of the most difficult tasks in artificial intelligence (Nti et al. J Big Data 7(1), 2020). Consequently, using machine learning techniques, it is possible to forecast how the insurance industry will perform on the day of its closing price. We collected data from Yahoo Finance for HDFC Life Insurance, Star Health, and Allied Insurance, SBI Life Insurance, ICICI Prudential Life Insurance, and Life Insurance Corporation of India from January 1, 2018, to December 31, 2022. The methodology of work is carried out using various models of machine-learning algorithms. The algorithms used to analyze company performance are Bayesian ridge regression, support vector machine, random forest regression, multinomial Naive Bayes, XGBoost regression, and Bayesian ridge regression. By employing these algorithms to analyze future share prices and boost the accuracy of stock price predictions (Nti et al. Open Comput Sci 10:153–163, 2020), investors can use the analysis to make investments in the company as well as buy insurance policies. They can also determine the most suitable forecasting models to produce the best estimations with the least amount of error (Nti et al. Appl Comput Syst 25(1):33–42, 2020).