Fluorescence intensity variation has long served as a primary readout for monitoring biological events. However, single-fluorophore signals arising from distinct molecular events often exhibit similar intensity profiles, making further classification challenging using conventional methods. In this study, we introduce smDeepFLUOR, a deep learning–based framework that resolves seemingly homogeneous spatiotemporal fluorescence signals by uncovering latent features imperceptible to conventional analyses. By leveraging a three-dimensional convolutional neural network trained on image sequences captured over 7 × 7 × 10 voxel windows, smDeepFLUOR reliably distinguishes specific from nonspecific protein binding, even across different experimental days, with an accuracy of up to 97%. Remarkably, smDeepFLUOR also captures real-time DNA synthesis kinetics by identifying subtle changes in the spatial distance between the fluorophore and the 3’ end of nascent DNA, a feature undetectable by traditional methods. These classifications were achieved without incorporating explicit physical rules or engineered features, implying the presence of intrinsic, previously unrecognized differences in emission patterns. This approach significantly extends the analytical capabilities of single-molecule fluorescence imaging and opens new avenues for minimally labeled and label-free protein activities.