Aquatic crops such as fox nuts (Euryale ferox) and water chestnuts (Trapa natans) play a vital role in the agroeconomy of the Indo-Gangetic plains, particularly in Bihar, India, which dominates global Makhana production. However, productivity in these wetland-based systems is severely constrained by uncontrolled aquatic weed infestation, leading to significant yield losses and high dependence on labour-intensive manual removal practices. This review synthesizes current challenges and emerging technological interventions, focusing on the integration of artificial intelligence (AI) and computer vision for real-time weed detection, identification, and precision management. The proposed AI-powered computer vision framework leverages drone-based multispectral imaging, underwater sensing, and deep learning algorithms such as YOLOv8, U-Net, and EfficientNet for accurate weed detection, segmentation, and classification. Coupled with GIS mapping, IoT integration, and robotic or precision herbicide delivery systems, this approach offers a scalable and sustainable alternative to conventional weed control methods. The review further highlights dataset development strategies, model pipelines, and field deployment mechanisms tailored for aquatic environments. Adoption of such intelligent systems is expected to enhance crop yields by 15–25%, reduce labor dependency by up to 60%, and minimize herbicide usage, thereby contributing to economic efficiency and environmental sustainability. This paper underscores the transformative potential of AI-driven precision agriculture in modernizing aquatic crop production systems and addressing critical challenges in wetland farming ecosystems.