Feature selection is the process in machine learning (ML) that allows for increasing the efficiency, interpretability, and correctness of any model by identifying the most significant features from a dataset. Traditional feature selection invariably presents the following challenges: a bias in data, variation via adversarial manipulation, or high computational inefficiency. It ends up with poor models, security problems, and ethical issues pertaining to the use of data. Blockchain technology, as an innovative and decentralized, transparent, and immutable solution to the aforementioned challenges, promises a change in how features are selected and their evaluation in ML applications through data integrity improvement, security enhancement, and a decentralized framework for feature evaluation. The chapter considers the integration of blockchain in feature selection: smart contracts, consensus mechanisms, and privacy-preserving techniques. The chapter also presents the current theoretical foundations and a thorough review of the available blockchain feature selection architectures, comparative studies with traditional methods, case studies from diverse disciplines (for example, health, finance, and supply chain management), and metrics for measuring performance. Lastly, it provides challenges and limitations currently faced in this emerging field, as well as future trends, paving the way for further studies and implementations in practice. Through this recent compilation, we have tried to demonstrate how innovative the future will be by using blockchain to change how feature selection would be conducted and improve innovativeness in ML in general.