The rising average temperatures of Earth’s surface, seas, and oceans constitute the primary drivers behind the increase in global temperatures, intricately intertwined with the issue of climate change. Global industrialization is among the main causes of the rise in global temperatures, which are constantly increasing. When compared to the beginning of the Industrial Revolution, this increase is approximately 0.98 °C. Projections derived from the analysis of trends spanning from 2000 to the present suggest that a temperature rise of 1.5 °C could be reached by 2030. Within the context of the current global situation, this paper aims to analyze temperature changes through an innovative technique: the smooth kernel distribution (SKD). This spatiotemporal framework provides a method for assessing the probability density function of a continuous stochastic variable, applied across both temporal and spatial dimensions, resulting in a four-dimensional function. This approach is based on constructing a comprehensive dataset that offers insights into the sequential temporal progression and geographical distribution of temperature metrics. The key strength of this method lies in its ability to seamlessly integrate temperature data, geographical coordinates, and the advanced application of the smooth kernel distribution. These elements synergistically produce results that offer greater flexibility for analyzing data across different scales, ranging from local to regional and national levels. This combination makes it possible to capture significant trends and to distinguish between occasional fluctuations and meaningful changes. The resulting maps offer a dynamic visual representation of temperature evolution over time and space.