This dissertation proposes a social learning-based distributed network time synchronization (SLDNTS) and compares it to a classic approach: consensus-based distributed network time synchronization (CDNTS) under a Global Positioning System (GPS) denial environment. An observation random variable (ORV), which is a conditional likelihood (e.g., Gaussian) given a synchronized true time hypothesis is used to generate clock times at each node and each iteration. This proposal will then show a simple method to construct an observation matrix that satisfies both the identifiability condition (IC) and the prevailing observation signal existence condition (POSEC) required for the social learning (SL). Practical clock parameters such as time offsets, frequency offsets, phase offsets, and observation noises are referred to the International Telecommunication Union (ITU) standard and considered in the evaluation. Then, this dissertation verifies, through simulation and the Allan deviation criteria to show that the proposed SLDNTS shows superior performance compared to the classic CDNTS under the same observation environment.