Abstract: The article presents a comprehensive review of wavelet analysis as an effective mathematical tool for improving the performance of artificial intelligence (AI) mod-els. The materials of the paper are of a scientific-methodological and review nature and are aimed at systematizing modern approaches to the application of wavelet transforms in intelligent data analysis. Modern AI systems operate on large volumes of complex and often non-stationary data, including time series, signals, and images, which require advanced methods for preprocessing and feature extraction. Traditional approaches, such as Fourier-based spectral analysis, are limited in their ability to capture local characteristics of signals in both time and frequency domains. In this context, wavelet transform provides a powerful alternative by enabling multiscale analysis and simultaneous localization of data features. The theoretical foundations of wavelet analysis, including continuous and discrete wavelet transforms, as well as multiresolution analysis, are examined in the paper. Particular attention is given to the ability of wavelet methods to decompose signals into components of different frequency ranges, which allows for the identification of both global trends and local fluctuations. This property makes wavelet analysis especially useful for handling noisy, nonlinear, and non-stationary data. The study focuses on the integration of wavelet-based techniques into modern artificial intelligence technologies. Specifically, the use of wavelet transforms for data preprocessing, noise reduction, feature extraction, and dimensionality reduction is analyzed. The paper also reviews recent approaches that incorporate wavelet operations directly into machine learning and deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models. Such hybrid approaches demonstrate improved robustness, better generalization capabilities, and higher prediction accuracy. Special attention is paid to the application of wavelet analysis in time series modeling, particularly in economic and financial domains, where data exhibit multiscale behavior and complex dynamics. Wavelet-based decomposition enables more accurate modeling and forecasting by isolating patterns at different temporal scales. The results of the study confirm that wavelet analysis is a powerful and versatile tool for enhancing the efficiency of AI models. The combination of multiscale signal processing and intelligent algorithms opens new perspectives for the development of advanced data analysis methods. Future research directions include the development of adaptive wavelet-based neural architectures and the application of wavelet techniques in big data and real-time AI systems