Pneumonia is a respiratory illness characterized by lung inflammation, leading to air sacs filling with fluid or pus. Symptoms include coughing with pus or phlegm, fever, chills, and breathing difficulties. To improve diagnostic decision-making, a research study proposes a deep transfer learning approach using the VGG16 convolutional neural network algorithm for interpreting chest X-rays.Transfer learning is an optimization technique that allows models to quickly adapt and perform better by leveraging pre-existing knowledge. In this case, VGG16 is trained on a large dataset of general images, and then applied to the task of classifying chest X-rays into two groups: pneumonia-related alterations or no pneumonia. The aim is to enhance the accuracy and speed of pneumonia detection.The research anticipates that the deep transfer learning model based on VGG16 will outperform current state-of-the-art approaches and popular ensemble techniques. By utilizing transfer learning, the model can effectively utilize the learned weights and knowledge from the general image dataset, enabling it to rapidly adapt to the specific task of pneumonia detection. This paper aims to optimize and evolve the method more efficiently, leading to improved diagnostic outcomes for pneumonia detection from chest X-rays.