The evolution of weather prediction models has been marked by significant advancements driven by scientific and technological progress. Early methods of forecasting relied on initially on the experiences and later observational data measured through scientific way with discoveries of important instruments and elementary empirical rules. The development of numerical weather prediction (NWP) in the mid-twentieth century marked a paradigm shift, utilizing mathematical models to simulate atmospheric processes. The advent of computers facilitated the implementation of complex algorithms and increased computational power, enhancing model accuracy and resolution. The integration of satellite data in the second half of twentieth century provided global observations, crucial for initializing and validating NWP models. Subsequent improvements in data assimilation techniques, model physics, and parameterization schemes further refined predictions. In recent decades, ensemble forecasting has become a cornerstone, offering probabilistic forecasts that quantify uncertainties. The emergence of machine learning and artificial intelligence has introduced new methodologies for pattern recognition and predictive analytics, complementing traditional NWP. Continuous advancements in high-performance computing and observational technologies along with emerging quantum computing promise to further enhance the precision and reliability of weather forecasts, underscoring the dynamic nature of this field.