The Indian Premier League (IPL), a significant cricketing event in India, features ten teams from various regions. With an emphasis on analyzing important performance indicators including batting averages, runs scored, wickets taken, and bowling economy, this study offers a thorough examination of the individuals on these teams. Data analytics are used in the study to produce an informative summary of player performance by utilizing IPL data from every season in which each player has played. The research underscores analytics’ pivotal role in strategic planning for games, which significantly influences team management. Analytics are also very important to the performance of betting sites and fantasy cricket platforms. The study uses deep learning methods to predict player performance based on metrics like bowler and batter averages, economy rates, wickets, and strike rates. With the use of performance measures like Mean Squared Error (MSE) and Mean Absolute Error (MAE), this study shows that Long Short-Term Memory (LSTM) networks are the most effective predictive model when compared to other deep learning models, such as Artificial Neural Networks (ANN), LSTM, and hybrid models that combine Convolutional Neural Networks (CNN) and LSTM. In order to advance cricket analytics, the study also highlights how important it is for stakeholders, data scientists, and cricket specialists to work together.