Indoor positioning in critical infrastructure is an emerging domain of research that is set to reinvent the way we navigate within GPS-deprived areas. The prolific pairing of smartphones and ubiquitous WiFi Access Points (APs) is expected to fuel the rapid adoption of this technology. However, maintaining long-term support for this technology undergoes unprecedented environmental changes over time owing to such factors as the gradual removal, addition, and replacement of APs. These changes lead to catastrophic degradation in quality-of-service. To overcome this challenge, this paper presents a novel set-and-forget framework using Laplacian Eigenmap manifold learning to enable long-term continual learning support for fingerprinting-based indoor positioning. An in-depth analysis of the proposed framework across a publicly available indoor UJI database shows improved positioning accuracy from 6% to 95% even in the presence of significant network configuration changes over time, when compared with the state-of-the-art.