Revolutionizing wireless traffic forecasting empowers proactive resource allocation, optimizing network performance and ensuring efficient utilization of resources in dynamic wireless environments. real-time traffic data from a business network with There are 470 APs.), this research provides a thorough examination of the temporal and geographical dynamics of network traffic. Time series data forecasting is given a new spin with the help of machine learning models built on the Transformer framework. This approach uses the brain’s attentional processes to analyze time series data for hidden dynamics and complex patterns. Notably, the analysis identifies high-traffic-utilization AP groups exhibiting robust seasonality patterns, alongside those devoid of such patterns. Several different types of forecasting methods are used and evaluated in this research, among them the Holt-Winters technique, a SARIMA model, a GRU model, a CNN model, and a model based on convolutional neural networks. In conclusion, the research sheds light on the complex patterns underlying network traffic and presents an innovative forecasting approach, bolstering the potential for improved wireless network resource management.