Abstract Water scarcity, rising energy costs, and declining irrigation efficiency are significant barriers to wheat production in Iraq. This study evaluates the economic, environmental, and sustainability impacts of integrating artificial intelligence (AI) into irrigation management under semiarid conditions. Field experiments conducted at the Al‐Ra'id Research Station in Baghdad during the 2025 season compared conventional diesel‐based irrigation with AI‐assisted irrigation that used soil moisture sensors, Internet of Things (IoT) controllers, and predictive weather algorithms. The analysis employed Cobb–Douglas production modeling, cost–benefit analysis, net present value (NPV), benefit–cost ratio (BCR), internal rate of return (IRR), and sustainability indices. Statistical validation using one‐way ANOVA confirmed that all observed improvements were highly significant, with treatment effects for wheat yield (F 1,18 = 1335.66, p < 0.001), water use (F 1,18 = 15228.16, p < 0.001), water‐use efficiency (WUE) (F 1,18 = 13065.49, p < 0.001), and energy consumption (F 1,18 = 24312.67, p < 0.001). The results demonstrate that AI‐assisted irrigation increased wheat yield by 35%, reduced water use by 36%, and decreased energy consumption by 30% ( p < 0.001). Economic evaluation indicated strong feasibility, with a NPV of USD 18,121, a BCR of 2.81, and an IRR of 30%, corresponding to a payback period of 3.65 years. WUE improved by 109%, and the Sustainability Efficiency Index (SEI) increased from 0.25 to 0.51. Sensitivity analyses confirmed that investment profitability remained robust under adverse scenarios, including increased capital costs and reduced wheat prices. These findings indicate that AI‐assisted irrigation substantially enhances productivity, economic returns, and sustainability outcomes. The adoption of AI technologies offers a scalable, resilient strategy for modernizing water management and promoting agricultural sustainability in Iraq.
Shayma AL‐Rubaye (Mon,) studied this question.