The piezoelectric performance serves as the basis for the applications of piezoelectric ceramics. The ability to rapidly and accurately predict the piezoelectric coefficient (d₃₃) is of much practical importance for exploring high-performance piezoelectric ceramics. In this work, a data-driven approach combining feature engineering, statistical learning, machine learning (ML), experimental design, and synthesis is trialed to investigate its accuracy in predicting d₃₃ of potassium–sodium–niobate ((K,Na)NbO₃, KNN)-based ceramics. The atomic radius (AR), valence electron distance (DV) (Schubert), Martynov–Batsanov electronegativity (EN-MB), and absolute electronegativity (EN) are summarized as the four most representative features in describing d₃₃ out of all 27 possible features for the piezoelectric ceramics. These four features contribute greatly to regression learning for predicting d₃₃ and classification learning for distinguishing polymorphic phase boundary (PPB). The ML method developed in this work exhibits a high accuracy in predicting d₃₃ of the piezoelectric ceramics. An example of KNN combined with 6 mol% LiNbO₃ demonstrates d₃₃ of 184 pC/N, which is highly consistent with the predicted result. This work proposes a novel feature-oriented guideline for accelerating the design of piezoelectric ceramic systems with large d₃₃, which is expected to be widely used in other functional materials.