Laser capture microdissection enables precise isolation of regions of interest (ROIs) from tissue sections, allowing researchers to investigate tissue heterogeneity with spatial resolution. This technique has been integrated with various omics approaches, including transcriptomics and proteomics, to enhance sensitivity and expand coverage beyond traditional bulk analysis methods. Among these, mass spectrometry (MS)-based proteomics has emerged as a particularly powerful tool by enabling the reliable identification of even low-abundance proteins and facilitating the discovery of novel disease biomarkers. Recently, both laser capture microdissection and MS-based proteomics have been significantly shaped by the rapid advancement and widespread adoption of artificial intelligence (AI). In fact, deep learning, a subfield of AI, has been used to improve bioimage analysis for ROI selection in laser capture microdissection workflows. Concurrently, AI-based methods are increasingly being used to manage and interpret high-dimensional data generated by MS-based proteomics, further enhancing analytical depth and reproducibility. Despite this progress, current workflows often lack scalability and flexibility, limiting their broader application in clinical and translational settings. To address these challenges, we have developed an AI-driven laser capture microdissection protocol optimized for hematoxylin and eosin (H&E)-stained slides derived from formalin-fixed paraffin-embedded (FFPE) patient samples. The widespread availability of FFPE tissues and the simplicity of H&E staining make this approach both accessible and adaptable for investigating diverse disease contexts, with the potential to advance spatial proteomics at scale.