Wheat as a staple crop supports the dietary needs of billions worldwide, making its quality vital for both nutritional value and industrial processing. High-quality wheat is essential for producing diverse end products such as bread, pasta, and other baked goods, which require specific grain and flour properties. Quality traits such as protein content, gluten strength, and kernel hardness directly influence milling performance and product quality. The integration of digital tools and artificial intelligence (AI) in grain storage, wheat quality assessment, and wheat-based production processes is transforming the wheat industry, enabling faster, more accurate, and scalable quality analysis. Technologies such as near-infrared spectroscopy (NIR), machine vision, and machine learning (ML) models are revolutionizing how wheat quality is evaluated, offering numerous advantages over traditional methods. The Internet of things (IoT) and ML are used on grain storage for better moisture control and spoilage prevention in silos. Predictive maintenance of milling and processing equipment is a proactive approach in the production sector that leverages technologies like IoT to optimize maintenance costs, reduce downtime, and enhance operational efficiency. With the advent of new technologies, particularly digital and AI approaches, the ways of analysing quality and managing production processes have been transformed. The 3D/4D printing can be used to produce wheat-based food items tailored to specific contexts and individuals with specific needs. This chapter highlights the role of digital and AI technologies in wheat quality evaluation, storage, and production processes, paving the way for a smarter and more sustainable future in the agriculture and food sectors.