Advanced high-strength aluminum (Al) alloys are essential for the generation of lightweight aerospace structural components. The load-bearing capacity and service safety boundaries of these components are directly determined by the alloys’ strength. The precipitation microstructures governed by the thermo-kinetic synergy of phase transitions provide the primary strengthening contribution of heat-treatable Al alloys. In general, experimental measurements of precipitation strengthening effects are limited by the insufficient spatial resolution of microscopic characterization and the restricted representativeness of statistical sampling. Additionally, theoretical models are restricted in their extrapolation capabilities when applied to complex multicomponent systems. In this work, we propose a machine-learning-assisted strength modeling strategy for heat-treatable Al alloys under the constraints of precipitation thermo-kinetic synergy. First, a dataset coupling the physicochemical properties of solute elements with respect to alloys’ composition, processing conditions, and mechanical properties is constructed. This dataset is used to train a predictive model for yield strength (YS) via Random Forest. Subsequently, quantitative relationships between yield strength, precipitation microstructures, and thermokinetic parameters are constructed by symbolic regression, providing the YS model with explicit physical interpretability. In combination with SHapley Additive exPlanations (SHAP) analysis of the YS model, the results indicate that introducing solute elements with higher group numbers enhance atomic bonding in the Al matrix while promoting the formation of strengthening precipitates, improving alloy strength. The SHAP results reveal that the precipitation driving force (ΔG_v), which depends on the alloys’ composition and aging temperature, is strongly associated with YS. Specifically, the precipitation strengthening contribution increases with the amount of ΔG dissipated during aging. Accordingly, increasing the initial precipitation driving force via compositional design and aging-process regulation can considerably improve the precipitation strengthening effects.