It is essential to have access to anisotropic location information to successfully execute localization-based healthcare system services in wireless sensor networks (WSNs). When it comes to large-scale WSNs, DV-Hop, a widely used range-free localization mechanism, is an approach that performs exceptionally well. The performance of the localization characteristics is extraordinary, even in networks that are spread in an equal manner. However, it exhibited a considerable lack of accuracy while working in networks that had various degrees of energy efficiency and differences in orientation. Range-free Node Localization (RNL) is proposed using the Range Reduction Localization. It is called as RNL-RRL protocol using Fuzzy Logic (FL) approach. This Artificial Intelligence (AI) technique takes into account several anisotropic and energy efficiency characteristics. Collinearity and compounded hop size inaccuracy are the primary factors that contribute to localization accuracy which is low and stability which is poor. The situation is exacerbated by both of these contributing elements. Another factor that makes matters more complicated is the selection of forwarding relays that are efficient in terms of the amount of energy they consume. Establishing a distance gap depending on the anchors reduces the degree of inaccuracy connected with the hop size assigned to the anchors. Simulation outcomes revealed that proposed protocol outperformed existing protocols in terms throughput, delay, energy consumption, and localization errors.