Adaptive signal filtering is an important part of modern signal processing tasks due to complex noise patterns that are frequently non-stationary along with non-linear behaviour of the signals. Although classical filtering techniques and traditional optimization methods are widely successful in clean scenarios, they are limited by rigid parameter setting, local minimum entrapment, and have poor self-adaptation ability to changing signal environment. In view of these facts, this technical note proposes an innovative bio-inspired computational model for adaptive signal filtering. Through the use of nature inspired algorithms namely PSO, GA as well as ACO the model dynamically tunes filter coefficients in real time resulting in effective, reliable and precise filtering of a varied range of signals. It has a better global search capability, can scale better to high dimensional filtering problems, and is more adaptable to real-time change in signal and noise characteristics. Comparison results show that compared with the previous conventional adaptive filtering techniques, the improvement of SNR, MSE and the convergence speed are remarkable. The proposed approach is particularly efficient for the biomedical signal augmentation, communication systems and smart sensor data manipulation. In this paper we connect these two areas by combining the principles of biological intelligence with those of real-time adaptive filtering, for complex real-world signal environments. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.