Efficient spectrum sensing is a critical aspect of Cognitive Radio Networks (CRNs), enabling the identification and utilization of underutilized spectrum bands while ensuring the protection of primary users. This research article introduces a novel approach to spectrum sensing by harnessing the inherent Cyclo-stationary properties of signals and employing specialized windowing techniques. The methodology demonstrated through simulation, capitalizes on the concept of Cyclo-stationarity to discern secondary users amidst the noise and varying signal conditions. The proposed technique involves processing received signals using tailored windows, revealing cyclo-stationary behavior that characterizes secondary user activity. Detailed plots visually illustrate key stages of the process, from noisy signal integration to Cyclo-stationary statistic calculation and the resulting detection outcomes. The article's contributions lie in offering insights into the potential of Cyclo-stationary analysis for spectrum sensing applications. The comprehensive analysis underscores the technique's efficacy in detecting secondary users and showcases its resilience in challenging wireless environments. The provided visualizations enhance understanding by demonstrating how the proposed method effectively separates signals of interest from noise. In summary, this research advances spectrum sensing methods for CRNs by introducing Cyclo-stationary-based window techniques. The presented approach offers a pathway to better spectrum utilization in dynamic wireless landscapes, contributing to improved network efficiency and reduced interference. The article provides a valuable reference for researchers and practitioners exploring advanced spectrum sensing strategies in cognitive radio systems.