The rapid evolution of smart materials is redefining engineering practice, replacing empirical trial-and-error with predictive, data-driven workflows. This chapter surveys the guiding principles, computational approaches, and outstanding challenges that underpin contemporary smart-material design. It explains how multiscale modelling, machine-learning surrogates, and multi-objective optimisation are coupled to prescribe composition, architecture, and processing routes that deliver targeted mechanical, thermal, electrical, and stimuli-responsive functions. Particular emphasis is placed on quantifying and controlling complexity—heterogeneity across length scales, interfacial phenomena, and emergent behaviours—using graph metrics, information-theoretic descriptors, and uncertainty quantification. Representative case studies from aerospace structures, flexible electronics, energy storage, and implantable biomedical devices demonstrate how integrated pipelines shorten development cycles and enhance sustainability by reducing material waste and energy demand. Persistent obstacles—limited high-quality data, sparse mechanistic insight, and opaque algorithmic predictions—are analysed, along with mitigation strategies such as active learning, interpretable models, federated data sharing, and hybrid physics–AI frameworks. The discussion also highlights the critical interplay between processing parameters and structure–property relationships, showing how additive manufacturing and in-situ characterisation feed directly into learning loops. Ethical dimensions are addressed through inputs for transparent model reporting and full lifecycle assessment. Special sections outline emerging open-source databases, community benchmarks, and standardised ontologies that will foster reproducibility and cross-disciplinary collaboration. Looking forward, the chapter argues that the convergence of high-throughput experimentation, physics-informed artificial intelligence, and digital-twin systems will enable continuous, closed-loop optimisation of material performance from synthesis to end-of-life, aligning materials innovation with circular-economy objectives. This integrated vision positions smart materials as key enablers of adaptable infrastructures, lightweight transportation, secure energy grids, and personalised healthcare, underscoring their central role in addressing twenty-first-century societal challenges. This integration of domain knowledge with free experimentation, the investigators can explore vast designs, structure–function relationship which will help fastening the idea to item adoption worldwide.