The rapid evolution of financial technology (FinTech) has transformed the way individuals’ access and use financial services. This study identifies the factors influencing FinTech adoption among users in the Surat district by focusing on two key constructs of the UTAUT2 model: Performance Expectancy and Personal Innovativeness. The study also examines whether demographic characteristics such as area of residence, gender, age, and monthly household income influence FinTech adoption. A descriptive and analytical research design was adopted. Primary data were collected from 220 respondents using a structured questionnaire through convenience sampling. Of these, 157 valid FinTech users were measured for reliability, regression, and other inferential analyses. Statistical techniques including Cronbach's Alpha, descriptive statistics, normality tests, Chi-square test, multiple regression, independent samples t-test, and one-way ANOVA were employed for data analysis. The findings indicate that the measurement scales possess excellent reliability. Performance Expectancy and Personal Innovativeness significantly and positively influence FinTech adoption, with Performance Expectancy emerging as the stronger predictor. The regression model explains a substantial proportion of the variation in FinTech adoption behavior. The study further shows that area of residence, gender, and monthly household income do not significantly influence FinTech adoption, whereas age has a significant impact, with younger respondents exhibiting higher adoption levels than older respondents. The study concludes that behavioral factors are more dominant than demographic characteristics in determining FinTech adoption. The findings provide valuable insights for policymakers, financial institutions, and FinTech service providers to develop strategies that enhance users' perceptions of usefulness and encourage innovation-oriented behavior, thereby promoting greater adoption of digital financial services across both rural and urban populations.