Biospeckle laser imaging is a non-destructive technique that is transforming the evaluation of quality and safety in food and agricultural products. This chapter explores its principles, methodologies, and applications, emphasizing its capability to assess key quality parameters such as firmness, ripeness, and microbial activity through the interaction of coherent laser light with biological tissues. Techniques such as Inertia Moment, Generalized Difference, and Time History Speckle Pattern (THSP) are examined, along with the integration of machine learning tools, including Artificial Neural Networks (ANN) and Support Vector Machines (SVM), to enhance predictive accuracy. The chapter highlights the versatility of biospeckle imaging across a wide range of food products, including fruits, vegetables, grains, and meat, demonstrating its effectiveness in detecting both internal and external defects without damaging samples. Case studies illustrate its role in identifying fungal infections, physiological changes, and texture variations, making it an essential tool for quality control during storage, transportation, and processing. While the technique offers numerous advantages, challenges such as environmental variability and calibration complexities remain. This chapter discusses these limitations and outlines potential pathways for future research and optimization. Designed for students, researchers, industry professionals, and policymakers, it underscores the significance of biospeckle imaging in advancing sustainable practices, reducing food waste, and meeting consumer demands for high-quality products. Ultimately, biospeckle laser imaging emerges as a transformative technology, aligning with the evolving needs of modern agriculture and food safety.