Bacteriophages (phages) are vital to bacterial ecology and promising tools for diagnostics and therapeutics, yet predicting and engineering host range remains challenging due to receptor diversity and bacterial defense systems. This dissertation integrates genomic analysis, computational modeling, and synthetic phage engineering to advance understanding and application of phage–host interactions. A newly characterized Escherichia phage (NRG-P0073-Ge15; 170,913 bp, 249 CDS) was sequenced and evaluated against the 72-strain ECOR library, revealing adsorption to 45.8% of strains but productive infection in only 20.8%, underscoring the complexity of receptor compatibility. To address rapid pathogen detection, an engineered Salmonella S16 phage was developed by incorporating a NanoLuc reporter and monomeric streptavidin tag for magnetic nanoparticle capture. This biosensor achieved detection of <10 CFU in 10 mL water within ~7 hours, with background luminescence reduced from 71,400 to 19 RLU after purification, demonstrating a practical, low-cost approach for food and water safety. To expand host range, modular adhesin engineering was applied to T4-like phages by swapping gp36–gp38 segments from T2 and T6, including a chimeric Gp38 design. Recombinant phages converted multiple E. coli strains from non-permissive to permissive hosts, though efficiency trade-offs highlighted structural constraints and the need for multi-component optimization. Finally, genome-informed reference frameworks were established to support predictive modeling. Genome scans showed that ECOR includes 219 of 517 curated phage–host interaction features (PHIFs), whereas the Bertrand Picard collection includes all 517 across 403 strains. Two curated panels were created: PHRECOR1 (119 strains) for diversity and PHRECOR2 (167 strains) for complete PHIF coverage. A Python-based inference tool linked host range phenotypes to genomic features, identifying candidate receptors and defense systems and enabling hypothesis-driven predictions. Collectively, this work delivers new phage resources, a validated rapid detection platform, strategies for host range expansion, and curated computational frameworks adaptable to foodborne pathogens such as Salmonella, Listeria, and Staphylococcus aureus. These contributions pave the way for data-driven phage selection and receptor discovery to enhance food safety monitoring, prevent contamination, and mitigate risks of antibiotic-resistant infections in the food supply chain.