Exploring Block Chain's Potential in the Global Food Supply Chain Through Machine Learning Analysis

In this research, aim is to assess the possibility of using the Block chain technology and machine learning in the global food supply chain revolution. Initial applications of PwC study have two main areas of focus: in compliance and certification audit support and monitoring sustainability practices. Random Forest had an accuracy of 92% in compliance and certification, specificity of 89% and 91%, respectively, sensitivity and F1 score to be 90%, which shows it was highly efficient to classify adherence to food safety regulations and certification standards. On the other hand, sustainability monitoring took huge profitability due to DBSCAN clustering, with a Silhouette Score of 0.75, which successfully detected sustainability clusters. The results show that Apriori association rule learning extracted strong relationships between sustainability practices and certifications, due to a high confidence level (0.85) and lift (2.10). t-SNE reduced the data to 2D dimensions which provides a great visualization of sustainability data (Explained Variance Ratio = 0.85, Reconstruction Error = 0.05). The current research lays stress on how Block chain and machine learning can actually transform performance of supply chain efficiency, transparency and sustainability. The encapsulated outputs and the overall process could be applied more generally to broader food supply chains, and complemented with state-of-the-art AI tools for predictive analytics as well as automated decision-making; thus, setting the stage for a new era of logistics and sustainability in logistical practices across the globe.