In the age of decentralized networks and Hadoop, the confluence of Blockchain & m/c learning (ML) technologies has emerged with a promising solution for ensuring robust, transparent, and private transactions. The benefits of Blockchain for the safety of data and ML provide effective efficiency. Their combination may revolutionize the retail industry, banking, Defense, etc., by offering unmatched data safety while preserving the best system performance. For instance, while some models may offer higher throughput, they could compromise on privacy levels. Conversely, models excelling in privacy preservation might incur higher energy costs or delays. This multi-faceted evaluation not only serves as a guide for stakeholders to choose the most efficient model based on individual or multiple metrics but also uncovers avenues for future research. By correlating these metrics with real-world use cases in healthcare, finance, and supply chain, among others, the paper adds a layer of practical applicability. This aids in the formulation of best practices for the deployment of these trending methodologies to maximize both performance and privacy. The paper contributes to the academic discourse by laying down a foundational framework for understanding and selecting the most appropriate blockchain-ML models for privacy preservation. The insights derived from this work are instrumental in steering the future development and deployment of secure, efficient, and privacy-preserving solutions across various industry verticals & scenarios.