The integration of deep learning into signal processing has changed how we decode & classify signals. This applies to areas, including neuroscience and wireless communication systems. In this paper, we together three new research studies that use deep learning techniques to improve signal processing. First, we look at convolutional neural networks (s). These are for decoding electrooculogram (EEG and local field potential (L) signals. The results show much better accuracy and efficiency in brain-computer interface (BCI) systems. Also, using these networks on IBM's TrueNorth neuromorphic chip shows it is possible to do real-time, exceptionally low power signal processing for neurobionics. Next, the paper reviews deep neural networks (DNNs) for channel decoding in communication systems. This study shows DNNs can reach near-optimal decoding performance, especially when using structured codes like polar codes. This study shows DNNs can reach near-optimal decoding performance, especially when using structured codes like polar codes. The networks can even do well with data they have not seen before. But there are some issues too. The curse of dimensionality and the difficulty of scaling these networks for longer codewords are important challenges discussed. Finally, we examine an adaptive ensemble deep learning model made for signal detection in Orthogonal Frequency Division Multiplexing (OFDM) systems. This model tackles big challenges in channel estimation and compensation by adjusting to changing conditions of multi-path channels. Because of this adjustment, there are major improvements in signal reliability & fewer symbol error rates (SER). Through these advanced methods, this paper highlights how deep learning impacts signal processing. It opens doors for future advancements in both neuroscience and wireless communication fields.