<p>The study investigates climate–agriculture interactions by integrating machine learning models with participatory household surveys, a novel dual approach in the context of climate change. We combined wheat crop statistics, vegetation indices, and climatic parameters from 2001 to 2021 with survey data from 292 farming households in Aligarh district, Uttar Pradesh, India. The temporal analysis revealed increasing trends in wheat yields and vegetation indices, with December–January being the months when EVI and NDVI showed strong positive correlations with wheat yields. Furthermore, vegetation indices were negatively associated with the diurnal temperature range but positively correlated with precipitation. March emerged as a crucial month, as rising temperatures, vapor pressure, and potential evapotranspiration had an adverse effect on vegetation indices. Among the models tested, random forest (RF) outperformed support vector machine (SVM) and multiple linear regression (MLR), demonstrating its robustness for predicting nonlinear crop–climate relationships. Household surveys revealed a high awareness of climate change, but significant barriers to adaptation were also reported, including a lack of capital and small landholdings. It indicated a need to connect awareness with adoption, which is crucial for successful adaptation interventions. By combining top-down (machine learning) and bottom-up (participatory) approaches, the research contributes a novel framework for climate-resilient policy planning. To the best of our knowledge, this is one of the first studies in India to integrate quantitative modeling with qualitative social insights at the localized district scale for a region underrepresented in climate–agriculture studies. The findings offer actionable insights for informed agrarian policy-making and lay a foundation for future integrative research on climate adaptation in rural areas.</p>