Considering climate change and the growing demand for food production, the nexus of artificial intelligence (AI) and digital agriculture offers a transformative potential for developing creative, climate-smart solutions to meet the problems of contemporary farming. AI technology integration in agriculture has chances to transform conventional methods. In areas, such as irrigation management, artificial intelligence (AI)-driven data analysis can offer affordable solutions for climate-smart digital agriculture. By integrating real-time weather data, remote sensing data, soil conditions, and crop health, farmers can make well-informed decisions that maximize resource use and boost productivity. Machine learning algorithms have the potential to accelerate the adoption of cutting-edge strategies to mitigate the effects of climate change, like anticipating agricultural water requirements in advance. Moreover, integrating AI-driven automation with well-calibrated remote sensing data might reduce the need for expensive sensor installations and expedite labor-intensive processes, resulting in increased productivity and scale. Although creating and implementing AI-driven solutions presents numerous hurdles, the promise is enormous. For AI systems to function, precise and localized data must be available. Resolving connectivity problems and data gaps continues to be a challenge. To improve the quality of the data, machine learning techniques have been applied to the detection of data anomalies and the imputation of missing values. There is no thorough data quality procedure in place for applying AI models to climate-smart agriculture. Using social network frameworks, lifecycle management, and capacity building for farmers and corporate stakeholders is necessary for the entire data-to-decision process, which includes AI modeling. To guarantee fair results, ethical issues like algorithmic bias and data privacy must also be properly navigated. In this paper, a comprehensive data quality process is proposed to enhance the accuracy of AI models for the effective rollout of climate-smart digital agriculture solutions.