Vernier effect-based optical fiber sensors have been demonstrated for high-sensitivity measurements of a diverse array of physical and chemical parameters. The interrogation of a Vernier sensor typically needs a broadband source and an optical spectrum analyzer to measure amplitudes over a broad wavelength window with dense sampling points, facilitating accurate extraction of the Vernier modulation envelope for sensitivity-improved sensing. However, the stringent requirement on the interrogation system limits the dynamic sensing capability of Vernier sensors. In this work, the possibility of employing a light source with a small wavelength bandwidth (35 nm) and a coarsely resolved spectrometer (∼166 pm) for the interrogation of an optical fiber Vernier sensor is demonstrated with the assistance of a machine learning-based analysis technique. Dynamic sensing of the exponential decay process of a cantilever beam has been successfully implemented with the low-cost and intelligent Vernier sensor. This work represents a first step towards a simpler, faster, and cheaper way to characterize the response of optical fiber sensors based on the Vernier effect.