This work is motivated by the imperative to overcome the limitations of conventional active noise control (ANC) techniques. These limitations are rooted in linear systems like the least mean square algorithm when faced with nonlinear distortions in the acoustic environment and system electronics, such as loudspeakers. This work addresses the nonlinear ANC problem by conceptualizing ANC as a deep learning problem. A novel stacked autoencoder (SAE) model is proposed, which is trained to estimate the noise that should be played at a loudspeaker so that the noise received at an error microphone may be attenuated. ANC experiments are initially set up in an anechoic room to study the fundamental performance characteristics of the architecture without extraneous considerations of room impulse response. This is followed by performance analysis in a normal reflective room. Actual noise, comprising broadband and tonal noise, is recorded in both an anechoic room and a normal reflective room. The importance of these experiments in the design process lies in their ability to account for nonlinear effects in actual setups, surpassing the limitations of relying solely on models. Experiment outcomes demonstrate that the proposed SAE model yields a noise reduction (NR) of up to 26.44 dB for wideband noise and up to 35.74 dB for tonal noise types. Additionally, there is an improvement in the extent of the quiet zone provided by the proposed SAE for tonal noise cases as compared to the traditional filtered-x least mean square (FxLMS) technique.