The integration of artificial intelligence (AI) and machine learning into plant breeding, known as the Breeding 5.0 paradigm, represents a significant leap forward in agricultural innovation. This systematic review explores how AI-driven approaches enhance precision, efficiency, and scalability in plant breeding by combining AI algorithms with big data analytics, high-throughput phenotyping, genomic selection, and environmental modeling. These advancements reduce time and costs, identify genetic traits, optimize breeding strategies, and predict plant performance. AI techniques such as image analysis and reinforcement learning accelerate breeding cycles and facilitate the development of resilient genes and climate-adaptive crops. Empirical evidence demonstrates substantial improvements, including up to a 25% increase in yield and a 30% improvement in disease resistance, for crops like wheat and rice​. Breeding 5.0 also enhances accessibility for a broader range of farmers, bolstering food security and promoting sustainable agricultural practices. However, challenges persist, particularly in data integration and accessibility. This review examines the core components of Breeding 5.0, including the role of AI in high-throughput phenotyping, smart breeding platforms, and deep learning for genomic prediction. It also addresses ethical considerations and potential challenges associated with the use of AI in plant breeding. Synergy between AI and plant breeding is paving the way for a new era of agricultural innovation, aiming to improve sustainable crop production and address global food security challenges.