Digital transformation (DT) has become a key catalyst in reshaping employment ecosystems and promoting inclusion for individuals with disabilities through the integration of technology and data-driven systems. The research aims to conduct a quantitative analysis of DT and its impact on employment inclusion for people with disabilities, utilizing big data insights derived from workforce and organizational databases between 2019 and 2024. A quantitative analytical framework was established using SPSS and AMOS to assess relationships among DT indicators such as technological readiness, accessibility infrastructure, and digital competence and inclusion-related variables including employment participation, job sustainability, and digital engagement levels. Statistical techniques such as multiple regression analysis, and Structural Equation Modeling (SEM) were applied to evaluate inter-variable associations and the extent of influence. Strong positive effects are shown by path coefficients (β) with TR → EP (H1-0.42), AI → EP (H2-0.35), DC → EP (H3-0.38), EP → JS (H4-0.47), and TR → DE → EP (H5-0.15), with all associations statistically significant. The model validates the strong influence of DT indicators on inclusive employment outcomes. Overall, the findings highlight that leveraging big data analytics can substantially enhance evidence-based strategies for promoting equitable and sustainable employment inclusion for people with disabilities in the DT era.