Cell counting is a fundamental task in various biological and medical research fields, providing crucial information about cellular populations in a variety of contexts. The addition of fluorescence microscopy has revolutionized cell imaging by enabling visualization of specific cell types and components with high precision and sensitivity. Thus, providing advanced techniques for distinguishing individual cells or cellular features, segregating clusters of cells by type, and even labeling distinct cells in culture or tissue section. However, the manual counting of cells in-situ is a time-consuming and subjective process prone to human error and cannot be performed from microscope images themselves. To overcome these limitations, researchers have turned to deep learning techniques, leveraging their ability to learn intricate patterns and relationships in large datasets. In this paper, we present a comprehensive approach for automated cell counting using deep learning algorithms applied to fluorescent microscopy images. We propose a novel framework that combines convolutional neural networks (CNNs) with advanced image processing techniques and statistical methods, enabling accurate and efficient cell quantification. Our method utilizes annotated training data to train the network, and subsequently employs it for automated cell counting in unseen microscopy images. We demonstrate the effectiveness and robustness of our approach through extensive experiments on diverse datasets, showcasing improved performance compared to existing methods. The proposed deep learning-based automated cell counting technique holds immense potential for accelerating research and advancing our understanding of various biological processes, while also serving as a valuable tool for diagnostic and therapeutic applications in clinical settings. In addition, we demonstrate the application of our model in various contexts including medical diagnosis, drug discovery, biological research, and environmental monitoring. With this research, we provide a foundation for future investigations in biomedical image analysis, offering new insights into the applications of deep learning in computer vision for medicine and healthcare.