Failure prediction in industrial systems constitutes a fundamental component for optimizing maintenance strategies, reducing operational costs, and ensuring safety within increasingly complex production environments. Conventional monitoring approaches, typically based on fixed thresholds or simplified statistical analyses, are often inadequate to capture the nonlinear, dynamic, and multi-scale behaviors that characterize modern industrial processes.  This study presents a comprehensive and critical comparative analysis of the principal intelligent algorithms, including machine learning, deep learning, and hybrid approaches, applied to industrial failure prediction. By systematically evaluating their respective strengths, limitations, and domains of applicability, the study highlights persistent challenges, particularly regarding model interpretability and robustness under real-world operating conditions. Building upon these observations, a novel hybrid architecture is proposed, integrating wavelet-based signal decomposition, convolutional neural networks for feature extraction, long short-term memory networks for temporal modeling, and evolutionary optimization techniques. This approach is designed to enhance predictive accuracy, improve resilience to noisy sensor data, and provide more interpretable outputs. Overall, the proposed framework contributes to the development of a new generation of predictive maintenance tools better suited to the complexity and variability of modern industrial systems.