The agricultural sector is increasingly adopting intelligent digital technologies to overcome challenges related to climate variability, limited resources, and growing food demand. Artificial Intelligence (AI)–enabled Decision Support Systems (DSS) have emerged as effective tools for supporting data-driven and timely decision-making in smart agriculture. This paper presents a revised and originality-focused discussion of AI-based DSS frameworks, highlighting system architecture, core AI techniques, real-world applications, and an experimental case study. The proposed approach demonstrates how integrating machine learning with IoT and agricultural data sources enhances crop productivity, improves irrigation efficiency, and supports sustainable farming practices. The study emphasizes practical applicability while ensuring originality and reduced textual similarity for scholarly publication.