<p>Chemical imaging probes enable the visualization of dynamic biology; however, engineering high sensitivity in live cells remains challenging. Here we present Sensight, a quantitative multivariate framework that integrates key photophysical and physicochemical descriptors to predict and optimize probe performance. Using a structurally diverse library, we identify five dominant parameters, define their optimal ranges, and unify them into a radar map representation with strong predictive power and intuitive visualization. This framework extends the structure–activity relationship analysis into imaging sensitivity, capturing complex structure–function relationships that shape probe behavior in live cells. Guided by Sensight, we design G₃, a superoxide probe with exceptional sensitivity for detecting early oxidative events. The framework’s generality is further demonstrated across distinct systems, including tetrazine–bicyclononyne bioorthogonal chemistry and formaldehyde sensing. Together, these findings establish Sensight as a predictive and generalizable strategy for high-performance probe design, with broad implications for sensing, imaging, and even theranostics.</p>