This thesis proposes novel active noise cancellation (ANC) algorithms based on deep learning to mitigate non-stationary and nonlinear noise. Traditional active noise control (ANC) methods face challenges when it comes to effectively mitigating construction-related noise, primarily due to the nonlinearity and transient nature of machinery sounds encountered on construction sites. In order to address this limitation, a highly effective feedforward ANC controller, named construction site noise network (CsNNet), has been developed utilizing advanced deep learning techniques. The proposed algorithm incorporates considerations for acoustic device delay and nonlinear characteristics, rendering it particularly suitable for open environments such as construction sites. Through extensive simulation studies, CsNNet demonstrated remarkable broadband noise reduction capabilities, achieving an average attenuation of approximately 8.3 dB across a wide range of construction-related noises. These results surpass the performance of both traditional ANC algorithms and contemporary state-of-the-art approaches. Additionally, CsNNet offers the advantage of scalability to multichannel ANC control without incurring additional computational costs, distinguishing it from previously developed ANC algorithms. Following extensive simulations and the development of the network architecture, we proceeded to assess the algorithm's performance through experimental testing in an acoustic environment. We carefully selected suitable equipment for the ANC system, including the microphone, loudspeaker, and signal acquisition device, prioritizing quality and minimizing delays. To accurately capture the characteristics of real-life acoustic and electrical secondary paths, we introduced a novel secondary path model based on deep learning. This model effectively addressed the limitations of traditional methods that relied on linear finite impulse response (FIR) filters for secondary path modeling. By incorporating this precise secondary path model, we conducted experimental tests on the causal version of CsNNet and observed a consistent agreement between the simulation and experimental results. The proposed algorithm is a significant contribution to the field of ANC using deep learning. It can be applied to various environments and has practical implications for noise control in different fields. The algorithm shows superior performance in controlling construction-related noise, which is a severe issue for governments in metropolitan cities. It has the potential to improve the quality of life in urban environments and reduce the impact of noise pollution on human health. The algorithm can also be used for noise control in other fields like transportation and aviation, where noise pollution is a significant issue. Overall, the thesis presents significant contributions to the field of ANC using deep learning-based algorithms, which have the potential to revolutionize noise control techniques. It is important to highlight that the content of this thesis is derived from our paper [1], with additional explanations provided for each section and an experimental investigation of the algorithm, along with the corresponding results.