Accurate assessment of balance is critical for fall prevention and targeted rehabilitation, particularly in older adults and individuals with neurological disorders. However, widely used clinical tools such as the Berg Balance Scale (BBS) are limited by subjectivity, reliance on controlled environments, and poor scalability, making them unsuitable for continuous or real‐world monitoring. In this study, a compact, wireless, and wearable system comprising three miniaturized inertial measurement units (IMUs) capable of assessing gait and balance with high reliability and simplicity are proposed. The system enables real‐time, synchronized acquisition of six‐axis motion data via wireless communication and timestamp‐based alignment. From these data, 10 clinically relevant gait features, including stride length, gait speed, rhythm, and double‐support duration, are extracted. The proposed system demonstrates high fidelity compared to a gold‐standard optical motion capture system (<6.6% deviation) and excellent repeatability across trials (<3% standard deviation), confirming its robustness for motion tracking. Based on these validated features, machine learning models are developed to predict BBS scores. Among them, the Random Forest algorithm achieves the highest predictive accuracy (R 2 = 0.804), with bootstrap analysis (95% CI: 0.53–0.81) supporting its statistical reliability. This work presents a scalable, low‐cost, and high‐fidelity alternative to conventional motion capture technologies, offering strong potential for clinical and remote balance assessments.