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Climate-smart agriculture (CSA) is a global development strategy aimed to address the interlinked challenges of food security and climate change. Expanding the implementation of conservation agriculture (CA), a vital component of CSA, is essential for enhancing agricultural and food security resilience while sustainably managing arable land. However, the extensive heterogeneity of biophysical and socioeconomic conditions presents significant complexities in promoting CA adoption. Addressing these challenges, this study carried out a comprehensive theoretical investigation of biophysical and socioeconomic factors influencing CA adoption and performance, integrating stakeholder feedback to create a systematic and robust evaluation index system for assessing CA suitability. By integrating multi-influencing factor techniques and fuzzy logic methods, we spatially identified suitable areas for CA implementation in China, providing valuable insights for land use policy. The reliability of the models was verified through a sensitivity analysis using the map removal sensitivity analysis method and the extended Fourier amplitude sensitivity test. The results indicated that 29.78% of the cultivated land was unsuitable or marginally suitable for CA, while 29.30 and 40.92% were determined to be moderately suitable and suitable zones, respectively. Suitable cultivated land was primarily distributed in the northern arid and semi-arid regions, the Loess Plateau, the Huang-Huai-Hai Plain, and the Northeast China Plain. Conversely, unsuitable, and marginally suitable cultivated land was predominantly located in the Qinghai Tibet Plateau, Middle-lower Yangtze Plain, Sichuan Basin and surrounding areas, the Yunnan-Guizhou Plateau, and Southern China. The topographical index, annual mean precipitation, humidity index, and population density were identified as the most significant factors influencing CA suitability. The CA suitability maps generated in this study will guide the development and extension agents targeting CA to suitable locations with a high potential impact, thereby maximizing the likelihood of adoption and minimizing the risk of failure.