The Xinjiang region’s ultra-deep reservoirs face extreme drilling challenges due to high temperature, pressure, tectonic complexity, and highly abrasive formations, causing rapid PDC bit wear and low ROP. To address this, a geomechanics-guided framework for personalized bit design was developed, integrating geological characterization, rock mechanics testing (UCS, BTS, AI), mineralogy (XRD), and microstructural analysis (SEM) to create formation-specific drillability profiles. A predictive ROP model combining MLR and machine learning (Random Forest, XGBoost) achieved R2 = 0.89 using 147 core samples and historical data. A custom hybrid PDC bit with 16 mm wear-resistant cutters, 10° backrake, optimized cutter density, and anti-whirl hydraulics was designed for the abrasive Bashijiqike Formation. Field testing in Well TW2 (5,334.91–6,254 m) increased average ROP by 20.2% (from 1.78 to 2.14 m/h) and reduced NPT from 18% to 10%, mainly due to fewer bit failures and improved stability. The study reveals that abrasivity index and quartz content (>70%) are more critical than UCS in predicting bit wear. This success validates a holistic, data-driven approach that balances durability and efficiency, offering a scalable solution for challenging basins worldwide.