Cell fate transitions are driven by regulatory circuitry, yet RNA velocity models cellular dynamics without explicitly accounting for gene regulatory interactions, limiting mechanistic insight. Conversely, gene regulatory network (GRN) inference methods largely neglect the dynamic nature of biological systems. To overcome this conceptual disconnect, we present RegVelo, a bottom-up, actionable, and interpretable deep learning framework that jointly models splicing kinetics and gene regulatory interactions. Across diverse biological systems, RegVelo provides reliable predictive power for terminal states, gene interactions, and perturbation simulations. By applying RegVelo to zebrafish neural crest development using full-length Smart-seq3 and shared gene expression and chromatin accessibility measurements, we delineate regulatory programs underlying fate specification. Guided by in silico perturbations and validated by CRISPR-Cas9 knockout and single-cell Perturb-seq, we establish tfec as an early driver and elf1 as a regulator of pigment cell fate. RegVelo establishes a quantitative framework for bridging gene regulation and cell fate decisions.