There are numerous methods for making sports predictions, and data analysis is crucial to predicting. Previous attempts in sports data analysis have resulted in the prediction of sports such as football, tennis next shot location prediction, Olympic athlete performance, basketball slam dunk shot frequency, and many more. Cricket prediction is tough due to the numerous variables that might affect the result or outcome of a cricket match. Previously, simple cricket match prediction systems focused on the venue, ignoring aspects such as weather, stadium size, captaincy, etc. Factors such as the match's location, pitch, weather conditions, first-pitch batting, and fielding all play a role in forecasting the match's outcome. To predict, suitable models are required, and data mining allows the required information to be extracted from data sets. This paper is a review of techniques used for predicting the winners of three different games. In order to anticipate various facts linked to a certain match, such as the outcome of the match, an injured player's performance in the match, the discovery of new talents in the game, etc., various machine learning algorithms can be used to exploit the statistical data of the game. The objective is to correctly forecast the outcome of a specific game.