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The millimeter-wave (mm-wave) frequency band is projected to play a critical role in next-generation wireless networks owing to its large available bandwidth. Despite the theoretical potential for high data throughput, the mm-wave frequency faces numerous challenges&#x2014;including severe path loss and high penetration loss. Therefore, a reliable understanding of channel propagation characteristics is required for the development of accurate and simple indoor communication systems. In this study, we conducted measurement campaigns with unique transmitter- receiver combinations using horn and tracking antennas, at 3.7 and 28 GHz in an indoor corridor environment on the <inline-formula> <tex-math notation="LaTeX">$10^{th}$ </tex-math></inline-formula> floor of an IT building and the <inline-formula> <tex-math notation="LaTeX">$3^{rd}$ </tex-math></inline-formula> floor of the main building of Chosun University, Gwangju, South Korea, and the details are presented herein. In both line-of-sight and non-line-of-sight scenarios, the large-scale path losses, and small-scale channel statistics, such as root mean square delay spread, and number of clusters, were obtained using the measurement results in a waveguide structure indoor corridor environment. We have proposed alternate methodologies beyond classical channel modeling to improve path loss models using artificial neural network (ANN) techniques&#x2014;to alleviate channel complexity and avoid the time-consuming measurement process. The presented regression successfully assists the prediction of the path loss model in a new operating environment using measurement data from a specific scenario. The validated results suggest that the ANN large-scale path loss model used in this study outperforms the close-in reference distance and floating-intercept (alpha-beta) models. Additionally, our result shows that the number of time clusters follows an Erlang distribution.