FSHD is a dominantly inherited, slowly progressive muscular dystrophy caused by deletion of repeats in the D4Z4 region on chromosome 4. Normal individuals have >10 repeats; patients with FSHD type 1 (FSHD1) have between 1-10 repeats. Little is known about predictors of functional burden. This study used concurrent epidemiological and machine learning (ML) techniques to analyze data collected in the US National Registry of FSHD to identify features that might predict functional outcomes. De-identified data from 578 participants with FSHD1 with an average of 9 years of follow-up reports were analyzed to assess interactions between characteristics including: age, gender, genetics (# of D4Z4 repeats), age of symptom onset and diagnosis, education, BMI, medication use and medical comorbidities to determine their influence on functional outcomes such as wheelchair use and job loss due to FSHD. These data were also used to develop ML random forest algorithms to identify risk factors that were predictive of wheelchair use (WC). Small allele size (1-3 D4Z4 repeats) was associated with earlier diagnosis (median 14 years, 95% CI 11, 17), facial weakness as the initial symptom (53.7%), and higher risk of WC. Across all groups, women were more likely to use a wheelchair (OR 1.74). Our final ML model revealed only a small contribution of genetics (lower risk for WC with 8-10 D4Z4 repeat units). The most significant predictors of WC were: disease duration, number of medications, age at diagnosis or symptom onset, and medical comorbidity (e.g. breathing difficulties, pneumonia, or arthritis), gender and BMI. A separate medications-only model predicated that all classes influenced towards WC except for amino acids. In conclusion, early ML modeling identified several features associated with WC use in FSHD, including number of medications and medical comorbidities, which might suggest aggressive medical management could be protective, but would require confirmation in additional data sets.