Accurate and reproducible bioimage analysis is crucial for understanding cellular structures and functions. Over the years, the field has progressed from relying heavily on manual analysis and classical image processing techniques to incorporate advanced machine learning and deep learning algorithms. These modern approaches have greatly improved key aspects such as preprocessing, segmentation, feature extraction, object tracking, and classification, enabling automated and reproducible analysis while minimizing subjectivity. The integration of classical and modern computational methods has significantly expanded the capabilities of bioimage analysis. User-friendly software tools and annotation platforms have further made these advanced techniques accessible to researchers lacking extensive computational expertise. This democratization has accelerated the adoption of machine learning in bioimage analysis across various biological disciplines. Real-world applications range from detecting subcellular structures to single-cell segmentation and tissue classification in clinical diagnostics. The ongoing synergy between traditional computer vision and deep learning continues to enhance bioimage analysis and interpretation, paving the way for new discoveries in biology and medicine. Consequently, this chapter explores the fundamentals, advances, and challenges in bioimage analysis, highlighting the integration of traditional methods and modern machine learning approaches to revolutionize the field.