<p>Recent developments in bioinformatics and artificial intelligence (AI) have significantly transformed microscopy image processing, leading to enhanced biological insights, increased automation, and improved accuracy. Traditional microscopy methods and manual image interpretation are time-consuming, subjective, and prone to human bias. The integration of advanced computational techniques—such as deep learning, machine learning, and sophisticated image processing algorithms—has led to substantial improvements in image segmentation, object identification, and feature extraction. AI-driven models, including generative adversarial networks (GANs) and convolutional neural networks (CNNs), have enabled advancements in image resolution enhancement, denoising, and quantitative analysis. Uniquely, this review emphasizes the convergence of microscopy with multi-omics datasets, facilitated by bioinformatics tools, providing an integrative framework for understanding cellular and molecular processes that is underexplored in previous reviews. These insights are proving invaluable in drug discovery, disease diagnostics, and biomedical research, where understanding intricate biological structures is critical. We also address current challenges—including data variability, model transparency, and scalability of AI applications—and briefly outline promising solutions, such as explainable AI, standardized data protocols, and self-supervised learning approaches. Finally, we discuss future directions, focusing on standardized data protocols, integration with quantum computing, and real-time imaging systems, all of which promise to further enhance computational microscopy in research and healthcare.</p>