Cross-linking mass spectrometry (XL-MS) is a transformative tool for probing protein structures. While conventional chemical crosslinkers exhibit well-defined chemistry for specific residues, photo-crosslinkers, despite their superior reactivity, have been hindered by incomplete mechanistic understanding and a lack of analytical frameworks. Here, we demonstrate that diazirine-based photo-crosslinks are inherently MS-cleavable—a novel property but also complicates spectral interpretation for overlapping peptide backbone and crosslinker side-chain fragments. Leveraging diagnostic side-chain fragmentation fingerprints, we developed a machine learning model that significantly improves ion coverage and reduces false discovery rates when combined with existing search algorithms. Further, we designed a homo-bifunctional diazirine crosslinker that allows for true on-demand photo-crosslinking. Empowered, we captured transient tetrameric assemblies of human HSP90β and revealed structural transitions in association equilibrium upon heat stress—features inaccessible with conventional chemical crosslinking. Together, our work establishes a new paradigm of XL-MS, combining the temporal sensitivity and precision of photo-activation with analytical confidence.