Fruit crops are of immense nutritional, economic, and cultural importance worldwide, yet their production is severely constrained by viral diseases that cause substantial yield losses and quality deterioration. Traditional virus management strategies—such as the use of virus-free planting material, vector control, and phytosanitation—remain largely preventive and are often inadequate due to rapid viral evolution, mixed infections, latent disease expression, and limited diagnostic resolution. This chapter highlights the transformative potential of multi-omics approaches as an integrative and sustainable strategy for fruit virus management. Using banana, citrus, and grapevine as representative case studies, the chapter reviews major viral pathogens, their epidemiology, and their impacts on crop productivity and quality. The application of genomics, transcriptomics, proteomics, and metabolomics is discussed in detail, demonstrating how these approaches collectively unravel host–virus interactions, identify resistance and susceptibility genes, and reveal metabolic and regulatory networks associated with viral infection. Advances in computational tools and data integration techniques—including network-based, Bayesian, correlation-based, fusion, and multivariate methods—are emphasized as critical for interpreting complex multi-layered datasets. Case studies illustrate how multi-omics analyses have enabled early virus detection, improved understanding of pathogenic mechanisms, and provided molecular targets for breeding and genome editing. Despite challenges such as data complexity, high costs, limited reference databases, and skills gaps, the chapter underscores the promise of integrating multi-omics with emerging technologies such as phenomics, artificial intelligence, and machine learning. Overall, multi-omics approaches offer a robust framework for developing data-driven, resilient, and sustainable virus management strategies to ensure long-term fruit crop productivity and food security.