CEST imaging is a promising tool for diagnosis and evaluation of treatment response in tumors. However, conventional CEST is not quantitative and requires long acquisition times. A recently developed technique, CEST MR fingerprinting (CEST-MRF), overcomes many of the technical limitations of conventional CEST but still suffers from limited volumetric coverage. In this work, we propose a novel multi-slice CEST-MRF pulse sequence and deep learning reconstruction method to enable volumetric coverage without the need for additional scan time. Numerical simulations and in vivo experiments in a healthy subject are performed to demonstrate feasibility and utility of the proposed multi-slice CEST-MRF technique.