Advances in phenotyping include high-resolution trait measurement integrated with multi-omics, systems biology, and advanced analytics, thereby bridging the genotype–phenotype gap and giving rise to integrative phenomics. Stress resilience in crops is often quantitative, polygenic, and strongly influenced by genotype × environment interactions. Precise, multi-temporal phenotyping is therefore essential to translate genomic information into field performance. Integrative phenomics combines high-throughput sensing (RGB, thermal, multispectral, hyperspectral, chlorophyll fluorescence, LiDAR) deployed on platforms such as unmanned aerial vehicles, proximal sensors, gantries, and satellites to generate multidimensional trait datasets. Machine learning for large-scale data integration, together with genomic prediction, genome-wide association studies, and digital twin simulations, enables fusion of phenomic, genomic, and environmental datasets into predictive selection models. Applications across abiotic and biotic stresses show how canopy temperature, vegetation indices (NDVI), fluorescence metrics, and root phenotypes improve quantitative trait loci discovery and genomic selection accuracy, enabling earlier and resource-efficient identification of tolerant genotypes. Integration with proteomics and metabolomics links biochemical mechanisms to observable traits and supports functional validation. Cost-effective and accessible approaches include regional phenotyping hubs, adoption of open data standards (BrAPI, PhenoApp), low-cost sensor arrays, and participatory breeding that engages local programmes. Emerging technologies like robotics, IoT sensor networks, portable spectroscopy, and artificial intelligence-driven analytics further strengthen the detective capabilities and adoption of the technology. Thus, breeding programmes must integrate high-throughput phenotyping tools along with advanced analytics for enhancing genetic gains for stress tolerance in breeding pipelines.