Bayesian predictive inference is at the core of the mathematical theory of inductive reasoning. Nowadays, this field has become very attractive especially for its connections with algorithmic probability, machine learning and artificial intelligence. The complexity of both problems and algorithm represents a constant source of research of asymptotic techniques, which are necessary to handle vast datasets. The present book contains the 11 papers accepted and published in the Special Issue "Bayesian Predictive Inference and Related Asymptotics-Festschrift for Eugenio Regazzini's 75th Birthday" of the MDPI Mathematics journal. The topics of the paper focus, among others, on Bayesian nonparametrics, species sampling models, partial exchangeability and optimal stopping. Finally, as the title suggests, the Special Issue aims to celebrate the 75th birthday of Prof. Eugenio Regazzini, who has provided so many important contributions to the field of Bayesian inference.