The field of image classification has made great strides with Machine Learning (ML) and Deep Learning (DL), but still encounters difficulties in attaining high efficiency and accuracy. VGG16 architecture, which is famous for its performance and depth, holds a chance for betterment when utilized together with advanced algorithms. Though VGG16 has been successful, there exists an opportunity to use techniques of dimensionality reduction as well as optimization to improve its performance. To this point, studies have mainly concentrated on using VGG16 for image classification. However, these methods mentioned above have not been thoroughly examined in combination. This research seeks to create a new image classification model through integrating VGG16 with Principal Component Analysis (PCA) and Bayesian-optimized algorithms. The aims are diminishing computational complexity, enhancing classification accuracy, and optimizing model parameters. The study concentrates on image classification tasks with publicly accessible datasets. It investigates the integration regarding DL together with statistic as well as optimization methods to handle the existing limitations. The research uses a two-step method. First, it employs VGG16 for extracting features and after that reduces its dimensions using PCA. Secondly, the research applies Bayesian Optimization (BO) for fine-tuning model parameters. The suggested approaches are assessed with the use of standard metrics and in comparison, with baseline models. The integration regarding VGG19 with BO and PCA showed an important advance in classification accuracy. The model which has been optimized through Bayesian method performed better than the baseline VGG16; it achieved a classification accuracy of 99.41%, whereas the model without any optimization reached only 56.54% accuracy rate. Moreover, for the optimized model but not using (BO), we got recall 56.54% precision 54.67%, and F1 score 54.38%. For the same one, but now with (BO), we saw results like this: precision at 99.42%, recall equal to or greater than 99%—both these measures improved significantly. The computational cost was brought down by using dimensionality reduction with PCA. The research brings an important contribution to image classification area because it suggests a robust model that merges DL with advanced statistical as well as optimization methods. The outcomes emphasize how much promise this combined technique holds for improving tasks related to classifying images.