Early stress detection in wheat (Triticum aestivum) is critical for minimizing yield losses under increasing biotic and abiotic pressures. This chapter provides a comprehensive review of advances in biomarker-based diagnostics, encompassing molecular, biochemical, physiological, and imaging-derived indicators. It integrates evidence from multi-omics strategies, high-throughput phenotyping, and sensor technologies, including transcriptomics, proteomics, metabolomics, chlorophyll fluorescence, hyperspectral and thermal imaging, Raman and infrared (IR) spectroscopy, biosensors, lab-on-chip platforms, and CRISPR-based diagnostics for pre-symptomatic detection of drought, heat, salinity, and pathogen-induced stress. The focus is on integrating molecular signatures such as stress-responsive transcription factors, specific miRNAs, variations in phytohormones, and reactive oxygen species (ROS) dynamics with proximal and distal sensing platforms, further enhanced by machine learning (ML) for improved specificity and scalability. Pathway-guided breeding and phenotyping approaches, UAV-based monitoring, wearable sensors, and artificial intelligence (AI-facilitated data fusion are also discussed. Critical issues related to genotype-by-environment interactions, cross-location validation, cost constraints, and the laboratory-to-field translation gap are highlighted. Suggestions are made for developing standardized protocols, interoperable data systems, and farmer-centric sensor designs. The chapter concludes with future perspectives, emphasizing that stable biosensors, integrated multi-omics approaches, and AI-powered monitoring can accelerate the development of climate-resilient wheat and safeguard global food security.