Discovering novel, unconventional optical designs in combination with advanced machine-learning assisted data analysis techniques can uniquely enable new phenomena and breakthrough advances in many areas including on-chip circuitry, imaging, sensing, energy, and quantum information technology. Topology optimization, which has previously revolutionized aerospace and mechanical engineering by providing non-intuitive solutions to highly constrained material distribution problems, has recently emerged as a powerful architect for advanced photonic design. Compared to other inverse-design approaches that require extreme computation power to undertake a comprehensive search within a large parameter space, topology optimization can expand the design space while improving the computational efficiency. This talk will highlight our most recent findings on 1) merging topology optimization with artificial-intelligence-assisted algorithms and 2) integrating machine-learning based analysis with photonic design and quantum optical measurements. Particularly, we will discuss our studies on implementing deep-learning assisted topology optimization for advanced metasurface design development, focusing on highly efficient thermal emitter/absorber development for thermal-photovoltaics applications. We will summarize our research on merging topology optimization technique with quantum device design for achieving ultrafast single-photon source that offers efficient on-chip integration. Finally, we will also describe our recent works on implementing a novel convolutional neural network-based technique for real-time material defect metrology at the quantum level that outperforms all existing approaches in terms of speed and fidelity. This new method rapidly extracts the values of the single-photon autocorrelation function at zero delays from sparse data and ensures one order speed up on solving “bad”/”good” emitter classification problem in comparison with conventional techniques.