<p>The soilless method of growing plants, known as hydroponics, has gained attention for its ability to increase crop yields while conserving resources. This paper presents a novel approach integrating Internet of Things technology with a Convolutional Reinforcement Learning-based Seahorse Search algorithm to optimize plant growth efficiency in hydroponic environments. The Convolutional Reinforcement Learning-based Seahorse Search architecture combines a Convolutional Neural Network for Extracting critical environmental features. Reinforcement Learning for adaptive control, and the Seahorse Search algorithm for hyperparameter optimization. The Convolutional Neural Network component enables precise feature extraction, ensuring accurate data-driven decisions. Reinforcement Learning introduces a self-adaptive mechanism, dynamically adjusting environmental conditions to meet plant growth needs, while the Sea Horse Search algorithm fine-tunes parameters to reduce resource wastage and prevent overfitting. Together, these components create a responsive and sustainable hydroponic system capable of optimizing plant health and yield. Experimental results using five hydroponic datasets such as the Lettuce growth days dataset, Hydroponic plants nutrition dataset, Hydroponic and soil compound dataset, nutrient solution and Pakchoi dataset, and Vertical farming dataset demonstrate the system’s ability. The proposed model attains a higher accuracy value of 99.11% in environmental condition management, with significant resource savings in water and nutrient usage. This proposed hydroponic system offers a scalable, automated solution for sustainable agriculture, enabling precise and efficient crop production. Through this work, we contribute to advancing Internet of Things-driven smart farming and highlight the potential of Artificial Intelligence-based optimization to revolutionize resource management in agriculture.</p>