Mushroom categorization is a difficult process since there are so many different species and they all have different aesthetic qualities. In this paper, we are to investigate the use of transfer learning techniques for precise mushroom classification. We have examined the performance of EfficientNetB0, ResNet50, InceptionV3, and MobileNet, four well-known transfer learning models. The mushroom dataset is first preprocessed to make sure that the images are properly scaled and normalized. After initializing and fine-tuning the pre-trained weights of the above-mentioned models on our mushroom dataset, we then apply transfer learning techniques. Each model is trained using a carefully chosen training set, and its performance is assessed using a different test set. Our findings reveal that all four transfer learning models successfully categorize mushrooms, achieving outstanding classification accuracies. With a 97% accuracy rate, EfficientNetB0 surpasses the other models when we compare them. Its sophisticated design, which combines compound scaling and AutoML approaches to attain the best accuracy and efficiency, is responsible for its exceptional performance. The results of this study demonstrate the value of transfer learning in tasks involving mushroom categorization. Additionally, they stress the benefit of using cutting-edge models like EfficientNetB0, which produce greater accuracy compared to conventional transfer learning architectures. The development of automated methods for mushroom identification and subsequent breakthroughs in the mycology field can be facilitated by the use of these findings by academics and professionals working on mushroom classification.