This research presents an artificial intelligence (AI)-driven machine learning (ML) approach for accurately measuring refractive index (RI) values across both lower and higher regimes than the fiber material's RI, using a simple single multimode interference (MMI) fiber sensor. The sensor configuration consists of a no-core fiber (NCF) segment between two single-mode fiber (SMF) sections. A Bilayer Neural Network (BNN) regression model is employed to predict both low refractive index (LRI) and high refractive index (HRI) regimes, achieving a broad dynamic measurement range from 1.3000 RIU to 1.3900 RIU for LRI regime and from 1.4600 RIU to 1.5500 RIU for HRI regime. The model demonstrates 99.7% accuracy and a low root mean square error (RMSE) of 0.0044, ensuring that predicted RI values closely match actual measurements without any RI ambiguity. Furthermore, the all-silica NCF structure is inherently resistant to temperature fluctuations, enabling its deployment in environments with varying temperatures without requiring additional temperature compensation mechanisms.
