Precise temperature regulation is essential for effective and safe magnetic resonance imaging-guided focused ultrasound (MRgFUS) treatments. Several variables influence the target temperature during sonication, with the energy delivered being a pivotal physical determinant. The skull density ratio (SDR) is utilized to evaluate the feasibility of treatment, with values below 0.3 – 0.4 generally considered contraindications for treatment. This study aimed to develop a robust predictive model for sonication parameters that can accurately achieve the desired temperature within the target tissue region. We obtained 152 treatment log data from the Insightec Exablate workstation. Variables, including power output, sonication duration, stop sonication button activation, SDR, age, sex, and initial sonication (ALIGN) parameters, were used as predictors (x), with the achieved temperature as the response (y), to construct the predictive models. RStudio was used to build linear models, and the TensorFlow library was employed for the neural network models. The linear and neural network models predicted tissue temperature with a mean absolute error of 2.78°C and 1.93°C, respectively, and a coefficient of determination of 0.71 and 0.76, respectively. The neural network model outperformed the linear model, demonstrating a smaller residual dispersion and a lower root-mean-square deviation. While the neural network model is more accurate and reliable for predicting MRgFUS temperatures, the linear model is easier to use. Key factors, such as sex, age, SDR, and the initial tissue response to the first sonication – the energy delivered during this initial treatment and the corresponding temperature – are crucial for optimizing subsequent sonication parameters such as power, energy, and duration.