<p>Food security remains a critical global challenge, especially in regions with arid climates and a growing population. This paper proposes a novel metaheuristic framework for optimizing annual crop land allocation, at national level, integrating real-world data and artificial intelligence (AI) techniques. A comprehensive literature review informs the selection of three metaheuristics: simulated annealing (SA), hill climbing (HC), and genetic algorithm (GA). These algorithms are tailored to optimize crop allocation across provinces, considering multiple objectives and carefully defined constraints, including crop priorities. Statistical data is collected to train a machine learning (ML) model to predict crop yields, providing additional input to optimization algorithms. The experimental results in Algeria highlight the GA superiority in solution quality and execution time, boosting production of high-priority crops among 36 selected crops, with notable improvements of +43% for soft wheat, +17% for potato, and +35% for tomato. GA outperformed SA by 5% and HC by 2%. Although the scarcity of historical data limits the model’s precision, it sets an order of magnitude to establish a national-scale land allocation, avoiding suboptimal crop planning, and providing actionable insights for regions facing similar challenges.</p>