This paper introduces machine learning applications in multidimensional signal analysis and shows how these technologies have transformed signal processing. Machine learning models, especially CNNs and RNNs, excel at handling complex multidimensional signals like images, videos, and time-series data. CNNs automatically extract hierarchical features from images and videos better than manual feature engineering. RNNs and LSTM networks capture temporal dependencies in sequential data, making them useful for sequence prediction and anomaly detection. Despite these advances, machine learning in this domain is difficult. High computational resources, large labeled datasets, and model interpretability are issues. Ensure robust performance across diverse and unseen data is another challenge. To solve these problems, data augmentation, synthetic data generation, and transfer learning are being investigated. The study shows that machine learning can improve signal processing accuracy, efficiency, and robustness, improving medical diagnostics, autonomous systems, and remote sensing. To overcome machine learning's limitations and fully utilize its capabilities, researchers, industry practitioners, and policymakers must collaborate and conduct ongoing research. The field can make groundbreaking advances and improve results across applications by addressing current challenges and building on these models’ promising capabilities. The findings provide a solid foundation for multidimensional signal analysis research and development.