This study explores the application of artificial intelligence (AI), machine learning (ML), and Genetic Algorithm in developing an optimal irrigation task scheduling system for smart agriculture. Smart agriculture integrates advanced technologies, including the internet of things (IoT), automation, and AI, to enhance farming practices, particularly in irrigation management. The objective of this study is to compare the performance of AI/ML models with Genetic Algorithm in scheduling irrigation tasks to increase crop yield and reduce water consumption. This aligns with the United Nations Sustainable Development Goals (SDGs) 2 –Zero Hunger and 6 –Clean Water and Sanitation. Data for this study was sourced from Kaggle data science repository, which contains 30,000 samples with 14 features. These features include soil and weather conditions such as soil moisture, air temperature, atmospheric pressure, rainfall, and levels of nutrients like Nitrogen (N), Phosphorus (P), Potassium (K), and others. The data was analyzed using various AI models, including Logistic Regression, Gaussian Naive Bayes Classifier, k-Nearest Neighbors, and Genetic Algorithm. The results indicated that Genetic Algorithm outperformed the other AI models, achieving an accuracy of 92.40%. This suggests that Genetic Algorithm is the most effective approach for an irrigation task scheduling system, promoting sustainable food production and efficient water management.