<p>Recent advances in sensor technologies and data acquisition platforms have led to the era of Big Data. The rapid growth of artificial intelligence (AI), computing power and machine learning (ML) algorithms allow Big Data to be processed within affordable time constraints. This opens abundant opportunities to develop novel and efficient approaches to enhance the sustainability and resilience of Smart Cities. This work, by starting with a review of the state-of-the-art data fusion and ML techniques, focuses on the development of advanced solutions to structural health monitoring (SHM) and metamaterial design and discovery strategies. A deep convolutional neural network (CNN) based approach that is more robust against noisy data is proposed to perform structural response estimation and system identification. To efficiently detect surface defects using mobile devices with limited training data, an approach that incorporates network pruning into transfer learning is introduced for crack and corrosion detection. For metamaterial design, a reinforcement learning (RL) and a neural network based approach are proposed to reduce the computation efforts for the design of periodic and non-periodic metamaterials, respectively. Lastly, a physics-constrained deep auto-encoder (DAE) based approach is proposed to design the geometry of wave scatterers that satisfy user-defined downstream acoustic 2D wave fields. The robustness of the proposed approaches as well as their limitations are demonstrated and discussed through experimental data or/and numerical simulations. A roadmap for future works that may benefit the SHM and material design research communities is presented at the end of this dissertation.</p><br>