Abstract: In response to issues such as distorted perception of downhole conditions and significant deviations between traditional rig monitoring data and the actual working state at the bit during underground coal mine drilling, this research aims to develop a compact, explosion-proof digital drill bit engineering parameter measurement system. The goal is to achieve high-precision, real-time monitoring of multiple parameters that accurately reflect the true rock-breaking conditions at the borehole bottom. An overall design for a small-sized, explosion-proof digital drill bit with an outer diameter of 120 mm was proposed. The design incorporates an offset layout for MEMS sensors and a circumferentially symmetric arrangement for strain gauges. It integrates multi-parameter sensing modules including strain gauges, accelerometers, an inner annulus pressure sensor, and a temperature sensor. Real-time data interaction with the Measurement-While-Drilling (MWD) system is enabled via wireless short-range transmission technology based on magnetic coupling principles. Focusing on the challenge of high-precision multi-parameter measurement within confined spaces, a decoupling algorithm for weight on bit (WOB) and torque based on a full-bridge strain gauge configuration was proposed. The disturbance patterns of temperature variations and differential pressure between inner and outer annuli on measurement results were systematically analyzed. A WOB-torque decoupling model incorporating these factors was established. Ultimately, a Φ120 mm explosion-proof digital drill bit was developed. The developed digital drill bit achieves high-precision synchronous measurement of WOB (0−150 kN, ±1.21% F.S.), torque (0−4 kN·m, ±0.94% F.S.), rotational speed (0−300 r/min), temperature (0−80 ℃), inner annulus pressure (0−15 MPa), and triaxial vibration (radial, tangential, and axial) (±16 g). Experimental results show that after optimization by the compensation algorithm, the measurement accuracy for WOB and torque across the full scale improved to ±1.21% F.S. and ±0.94% F.S., respectively. Field trials demonstrated that the system can effectively identify abnormal conditions such as sudden changes in torque and WOB, as well as lateral vibration based on the measured data. Comparative data revealed that the average WOB measured at the bit was 9.4 kN lower than the feed force monitored at the rig, intuitively highlighting energy consumption factors like borehole wall friction and confirming the necessity of directly acquiring bit working parameters. The research outcomes provide direct data support and a hardware foundation for the digital perception, condition identification, and intelligent control of the drilling process in underground coal mines. Future research can focus on intelligent diagnosis of drilling states and adaptive control strategies based on the multi-source data from the digital drill bit, to further advance the development of safe, efficient, and intelligent drilling technology in coal mines.