Using cutting-edge neural network designs, the study “Advancements in Lung Cancer Prediction: An In-depth Analysis and Optimization of Neural Network Algorithms VGG16, InceptionV3, and EfficientNetB0” explores the field of lung cancer prediction. This study examines the performance of three well-known models: VGG16, InceptionV3, and EfficientNetB0, with an emphasis on improving prediction accuracy. By means of comprehensive research and optimization methodologies, the initiative endeavors to attain noteworthy advancements in precision levels. The experimental findings demonstrate the usefulness of these algorithms in lung cancer prediction tasks, with obtained accuracies of 80%, 77%, and an astonishing 93%, respectively, indicating promising results. With the potential to improve patient outcomes and healthcare practices, this research adds to the continuing efforts to use machine learning for early identification and prognosis of lung cancer.