This study presents a novel framework for detecting asynchronous periodic patterns in multivariate time series data. As the complexity and volume of time-dependent data continue to grow across various domains, there is an increasing need for robust methods to identify recurring patterns that may not align perfectly across different variables or dimensions. Our proposed framework integrates advanced signal processing techniques with machine learning algorithms to address the challenges of detecting periodicities in high-dimensional, noisy, and potentially misaligned data streams. Key components of the framework include adaptive filtering, dimensionality reduction, and a modified autocorrelation analysis that accounts for phase shifts and variable periods across different dimensions. We evaluate the performance of our framework on synthetic datasets with known periodic structures, as well as real-world multivariate time series from diverse fields such as finance, healthcare, and environmental monitoring. Results demonstrate significant improvements in both accuracy and computational efficiency compared to existing methods, particularly in scenarios with complex, interleaved periodic patterns. This framework provides researchers and practitioners with a powerful tool for uncovering hidden periodicities in multivariate time series, potentially leading to new insights and predictive capabilities across a wide range of applications.