Even though thermodynamic stability-oriented crystal structure prediction (CSP) has revolutionized materials discovery, the energy-driven CSP approaches often struggle to identify experimentally realizable metastable materials synthesized through kinetically controlled pathways, creating a critical gap between theoretical predictions and experimental synthesis. Here we propose a synthesizability-driven CSP framework that integrates symmetry-guided structure derivation with a Wyckoff encode-based classification model, allowing for the efficient localization of subspaces likely to yield synthesizable structures. Within the identified promising subspaces, a structure-based synthesizability evaluation model, fine-tuned using recently synthesized structures to enhance predictive accuracy, is employed in conjunction with ab initio calculations to systematically identify synthesizable candidates. The framework successfully reproduces 13 experimentally known $X\mathrm{Se}$ ($X=\mathrm{Sc}$, Ti, Mn, Fe, Ni, Cu, and Zn) structures, demonstrating its effectiveness in predicting synthesizable structures. Notably, 92 310 structures are filtered from the 554 054 candidates predicted by Graph Networks for Materials Exploration, exhibiting promising synthesizability. Additionally, eight thermodynamically favorable $\mathrm{Hf}\text{−}X\text{−}\mathrm{O}$ ($X=\mathrm{Ti}$, V, and Mn) structures have been identified, among which three ${\mathrm{HfV}}_2{\mathrm{O}}_7$ candidates exhibit high synthesizability, presenting viable candidates for experimental realization and potentially associated with experimentally observed temperature-induced phase transitions. This work establishes a symmetry-guided and data-driven framework for machine-learning-assisted prediction of synthesizable inorganic materials, highlighting its potential to bridge the gap between computational predictions and experimental realizations while unlocking new opportunities for the targeted discovery of novel functional materials.

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• Crystal structures
• Density functional calculations
• High-throughput calculations
• Machine learning