Plant phenotyping is being transformed by advances in artificial intelligence (AI), addressing key issues of climate change and global food security. Current plant trait assessment methods are limited in their scalability and labor intensive, making them unsuitable for modern agriculture. In this study, the research work shows how integrating advanced AI techniques, such as machine learning and computer vision, can not only revolutionize plant phenotyping, but also increase agricultural productivity. High-resolution imaging, real-time monitoring and predictive modeling are incorporated in the proposed methodology. These algorithms take in huge amounts of data about sensors, drones and imaging technologies, automating analysis of key traits, like growth patterns, disease resistance and stress tolerance. That means researchers can see which genetic variations and optimal growth conditions yield the best in terms of crops with high yield potential and climate resilience. The benefits of AI-driven phenotyping extends beyond facilitating research processes, it enables farmers to use those insights to optimize the use of resources and increase yield. The integration of these technologies combines to enhance precision agriculture practices, limiting waste, increasing efficiency and encouraging sustainability. The potential of AI to transform agriculture is highlighted in this research, and provides scalable, sustainable solutions to broader global food challenges. Future directions are to explore whether population and disease has any effect to push IoT, drone technologies and genetic data to further advance phenotyping techniques.