This paper presents the development of an intelligent temperature to frequency converter to measure the temperature using a negative temperature coefficient (NTC) thermistor. The signal conditioning circuit (SCC) of the NTC thermistor is a modified timer circuit whose control voltage is generated by a difference amplifier. The thermistor SCC acts as a temperature to frequency converter and exhibits a moderate linear temperature-frequency characteristic over a range of 0–100 °C with a linearity error of ±3.5%. A multilayer perceptron (MLP) neural network with Levenberg–Marquardt (LM) algorithm is used for modeling and nonlinearity estimation of the converter. The LM algorithm effectively reduces the linearity error as compared to the Broyden–Fletcher–Goldfarb–Shanno (BFGS) and the scaled conjugate gradient (SCG) algorithms. Mean square error (MSE), regression coefficients, linearity, accuracy and dispersion spread are used to evaluate the performance of the proposed ANN-based modeling. The intelligence of the ANN-based modeling is embedded in a low cost microcontroller unit and the performance is experimentally verified on a prototype unit. The linearity error and sensitivity of the proposed unit are approximately ±0.35% and 5 kHz/°C respectively.