The integration of Artificial Intelligence (AI), sensor technologies, and robotics is transforming plant phenotyping and precision agriculture, offering advanced methods to enhance crop management and improve resource efficiency. High-throughput phenotyping (HTP), which utilizes cutting-edge tools to rapidly measure plant traits, plays a key role in this transformation. The use of robotic platforms, imaging technologies, and machine learning (ML) algorithms allows for accurate and efficient analysis of plant characteristics. These systems rely on various environmental parameters, such as hyperspectral imaging to measure pigments, thermal imaging for water content, and fluorescence imaging to assess photosynthesis. AI and ML techniques are employed to process large volumes of phenotypic data, enabling predictions about growth rates, irrigation needs, and early detection of diseases, nutrient deficiencies, and pests. By optimizing environmental factors and incorporating precision breeding strategies, these technologies contribute to enhanced crop productivity, yield improvement, and better resource utilization, advancing sustainable agricultural practices and strengthening food security in the face of growing global demands.