Time series forecasting is a critical endeavor spanning diverse domains such as finance and medicine, demanding accurate predictions derived from historical data. The complexity escalates when analyzing seasonal data, characterized by unpredictable fluctuations and diverse influences, thereby challenging the suitability of existing AI tools. Hence, a meticulous approach encompassing thorough data analysis, model selection, and effective strategies is imperative. This paper aims to comprehensively examine and compare state-of-the-art time series forecasting models tailored for seasonal data, with a focus on addressing associated challenges and enhancing forecast accuracy. Seven models, including Temporal Fusion Transformer, DeepNPTS, PatchTST, DeepAR, NPTS, DLinear, and WaveNet, were identified through a thorough review, delineating their principles, advantages, and limitations. These models, along with a basic naïve approach, were rigorously evaluated using diverse performance metrics, particularly within the context of product sales data analysis. Our analysis highlights neural network models leveraging transformer architectures as exhibiting superior efficacy in achieving high-precision forecasts for seasonal time series data. However, it is noted that these models require a substantial time investment for parameter optimization, often involving thousands of parameters. These findings contribute to the advancement of time series forecasting methodologies, facilitating practical applications across various domains.