The majority of airport runways in Indonesia are constructed using flexible pavements, which are significantly affected by intense loadings from traffic and environmental conditions. Among these, temperature stands out as the critical factor influencing the Hot Mix Asphalt (HMA) layer’s performance due to its viscoelastic properties. Understanding the maximum temperature profile within the HMA layer is essential for assessing the pavement’s load-bearing capacity. However, most existing temperature prediction models are tailored to four-season climates, leaving a gap in models that are suitable for tropical environments, similar to that of Indonesia. To address the issue, this study introduces a novel maximum temperature prediction model for the asphalt layer, leveraging data from the innovative Airside Pavement Sensing System (AirPaSS). This model is based on direct temperature readings collected at various depths over 141 days, at 15-min intervals, from the Labuan Bajo Komodo Airport (LBJ) runway. Employing linear regression techniques, the model was meticulously developed and validated for a broader application across different airports. Results confirmed the model’s efficacy in accurately forecasting the HMA layer’s maximum temperature distribution. It offers a significant advancement in pavement engineering for tropical regions.