This study presents a literature review that traces the evolution of decision support for crop production, from process-based yield modeling through precision data sources to the application of artificial intelligence and hybrid models. Its aim is to provide a comparative analysis of how DSSAT, WOFOST, and AquaCrop-type models, as well as sensor, remote sensing, and yield mapping data, and AI-based methods, enhance the reliability of crop production decisions under various decision-making scenarios and conditions. The main finding of the review is that the practical value of decision support does not stem from the application of a single model or technology, but rather from the integration of scientifically sound models, quality-controlled site-specific data, and interpretable, adaptive algorithms. The study concludes that the key to domestic applicability lies in local calibration, strengthening long-term experimental and operational databases, and developing user competencies.