Microorganisms, comprising bacteria, viruses, fungi, and protozoa, are pivotal across various domains including human health, agriculture, biotechnology, and environmental science. Understanding these microorganisms is essential for advancements in these fields. This abstract highlight the significance of studying microorganisms and highlights key research areas and microscopy techniques utilized in their analysis. This paper presents a deep learning approach for microbacteria image classification using Convolutional Neural Network (CNN) architectures. The study focuses on accurately distinguishing between different microbacteria species, including amoeba, euglena, hydra, paramecium, and yeast. Various CNN architectures such as LeNet and VGG are explored, and the dataset comprises carefully annotated images representing different microbacteria species for training and testing the model. Through experimentation, the proposed model achieves high accuracy, reaching 94% in classifying microbacteria species. The performance of different CNN architectures is evaluated, providing insights into their strengths and weaknesses. Additionally, challenges encountered during model development are discussed, along with proposed solutions for improving accuracy and efficiency. Overall, this research contributes to the field of microorganism classification by demonstrating the effectiveness of deep learning approaches, particularly CNN architectures, in accurately identifying and classifying microbacteria from images.