Recent advances in materials science have been driven by the powerful integration of experimental techniques, computational modeling, and data-driven approaches. This chapter presents a comprehensive overview of state-of-the-art characterization methods, including advanced X-ray diffraction, electron microscopy, spectroscopy, and in-situ/operando techniques that enable atomic-to-macroscale insights into materials behavior. On the computational side, methods such as density functional theory, molecular dynamics simulations, and high-throughput screening have significantly accelerated materials discovery by predicting properties with high accuracy and guiding experimental validation. Machine learning and artificial intelligence have emerged as transformative tools for materials informatics, property prediction, and autonomous discovery. The chapter also highlights specialized classes of materials, including two-dimensional materials, high-entropy alloys, biomaterials, and sustainable materials, showcasing how the convergence of experimental and computational strategies enables the rational design of materials with tailored properties. Future directions emphasize the integration of quantum computing, advanced machine learning paradigms, and closed-loop discovery systems to address global challenges in energy, healthcare, and environmental sustainability. By bridging experimental and computational advances, materials science is entering a new era of accelerated innovation, enabling the creation of novel materials with unprecedented functionalities.