Background and Objectives: Approximately 28-35% of people aged 65 and older fall each year. As most of the falls occur during walking; evaluation of gait and balance impairment are considered as the most prevalent and sensitive predictors of fall. The population of older adults is expected to increase 193 million by 2050 in India. However, no such programme fall prevention programme exists to date in India. Therefore, the present study was undertaken to establish normative reference gait parameters for Indian older adults and identify older adults at risk of fall using wearable sensors. Methodology: This cross-sectional study was conducted in the Pune city, India among 659 community-dwelling older adults. Participants performed the Timed-up and go test (TUG) test fitted with the wearable sensors, which measured 59 gait parameters. Seven parameters were standardized for Indian older adults and fall risk of each individual was computed. Independent t-test, and one-way ANCOVA were used to establish normative gait parameters and predict risk of fall. Results: The study reported a fall prevalence of 24.7%, with a mean stride length of 123.00 ± 15.19 cm and stride velocity of 110 ± 17.57 cm/s respectively. Significantly (p<0.05) shorter stride length was observed in participants above 80 years of age (109.01+18.08 cm). Using these gait parameters, the study found that 20% of the study participants were at low risk of fall, 30.5% medium risk, 25.5% had high risk and 24.1% were at very high risk of falling. The sensor reported a sensitivity of 85.71% (CI: 69.74%- 95.19%) and 56.02% (CI: 49.82%- 62.07%) specificity to predict falls. Conclusions: Wearable sensors are a dependable tool for identifying older adults at risk of falling in community settings with limited resources and expertise.