Machine learning and compression have been described as "two sides of the same coin", and the exponential amounts of data being generated in diverse domains underscores the need for improved compression as well as efficient AI systems. Leveraging deep generative models, recent machine learning-based methods have set new standards for compressing images, videos, and audio. Despite these strides, significant challenges, such as computational efficiency and theoretical limitations, remain. Parallel advances in large-scale foundation models further requires research in efficient AI techniques such as model compression and distillation. This workshop aims to unite researchers from machine learning, data/model compression, and information theory. It will focus on enhancing compression techniques, accelerating large model training and inference, exploring theoretical limits, and integrating information-theoretic principles to improve learning and generalization. By bridging disciplines, we seek to catalyze the next generation of scalable, efficient information-processing systems.