Artificial Intelligence (AI) and Machine Learning (ML) have changed a variety of industries, including food. This study investigates sophisticated modeling techniques using AI and ML to improve food quality, from farm to fork. AI-driven predictive analytics and machine learning algorithms are used to monitor and control different phases of food production, processing, and distribution. Precision agriculture uses AI models to optimize planting, irrigation, and harvesting, resulting in greater crop yield and quality. During processing, ML techniques such as image recognition and spectroscopic analysis are employed to detect pollutants and assess food quality in real time, assuring safety and standard compliance. Predictive models are used in packaging and storage to anticipate shelf life and identify spoiling, minimizing waste while maintaining quality. AI-powered supply chain management solutions streamline logistics, guaranteeing that consumers receive fresh, high-quality food. Case studies of successful AI and machine learning deployments in diverse food businesses are presented, revealing the actual benefits and potential limitations of these technologies. The study finishes by discussing ethical implications, data privacy concerns, and the need for legislative frameworks to enable the responsible use of AI and machine learning in food quality development. This comprehensive assessment highlights AI and ML's transformative potential in enhancing food quality, safety, and sustainability, and advocates for their widespread use and continual innovation in the food industry.