Climate-smart agriculture technology (CSAT) safeguards farm income from climate-induced crop loss while lowering emissions. The study, being a pioneering one in a developing country context like Bangladesh, aims to identify the factors determining the adoption of CSAT and its impact on multidimensional poverty (MPI) among climate-vulnerable farm households, which is a novel contribution to the field of sustainable agriculture. The study, administering a simple random sampling, surveyed 351 farm households from three coastal subdistricts of southwestern Bangladesh. The study deployed the full information maximum likelihood (FIML) estimation method, along with the endogenous switching regression (ESR) approach to incorporate counterfactuals. The result postulates that farm income, paddy yield, access to extension service, and digital info determine the CAST adoption decision. The average treatment effect (ATT) claims that the adopters of the CSAT are more likely to reduce MPI (-0.164, p < 0.01) than the non-adopters. The counterfactual effect (ATU) also demonstrates that current non-adopters would minimize MPI significantly had they adopted the CSAT (0.054). Therefore, training the farmers on CSAT and its decentralization through a separate government wing can boost CSAT adoption and agricultural yield, and remove barriers such as accessing accurate climatic data, thus, leading to curbing poverty in climate-hit zones.