Pneumonia is a leading cause of death worldwide, and early detection is crucial for successful treatment. There is vast literature available for this study. The goal of this study is to study existing models from these literatures for pneumonia prediction using artificial intelligence (AI) techniques. The proposed AI model utilizes a dataset of patient information, including clinical symptoms, laboratory results, and radiographic imaging, to predict the likelihood of pneumonia. The model employs several machine-learning algorithms, including logistic regression, decision trees, and support vector machines, to analyze the data and generate predictions. The existing techniques are studied and analyzed in depth to develop a model for new method that optimizes the pneumonia prediction with greater accuracy. The results obtained through exhaustive study of literature gives the direction for the researchers to enhance the previous work and to develop state-of-the-art models.