Incorporating Z-transform techniques into current prediction models is a novel method presented in this paper for increasing prediction accuracy across a variety of domains. The ability to make accurate predictions is crucial in a variety of industries, including finance and weather forecasting. This study investigates the possibility of Z-transform augmentation to improve predictive performance. Although the Z-transform is a frequently utilized mathematical technique in signal processing and control systems, its usage in predictive modeling is still largely unexplored. This work shows how it is possible to incorporate Z-transform ideas into prediction models that can produce estimates and forecasts that are more accurate. For these estimates, Z-transform augmented prediction models, a potent technique in signal processing and time series analysis, can be applied. By changing and analyzing data in the Z-domain, which is a frequency-domain representation of a time series, they can be utilized to increase the precision of forecasts. In this research, a novel method for protecting data privacy while maximizing the forecasting accuracy of Z-transform augmented prediction models is provided. This paper presents a unique framework for protecting sensitive time series data without sacrificing predictive analytics, which seamlessly combines the Z-transform method and the Advanced Encryption Standard (AES) encryption.