One of the most important winter-sown legume crops worldwide is the faba bean. It is important to optimize the productivity of stable cultivars under favourable environmental conditions, agricultural practices, and phosphorus fertilizer to increase faba bean production especially, under climate change. The study was conducted in the two different locations, El-Kharga-New Valley and El-Tur location, South Sinai, during two successive seasons, 2020-2021 and 2021-2022, to study the response of some faba bean cultivars to phosphorus fertilization under El-Tur and New Valley conditions. The experiments included four faba bean cultivars i.e., Sakha1, Espain, Mariout 2 and Giza 843 and three phosphorus levels i.e., 0, 25and 50 kgP2O5\fed (12.5%P2O5). The results indicated that Sakha1cultivar produced the highest seed yield and its components. Fertilizing faba bean plants with 50 kg P2O5 produced the highest value of growth characters, seed yield and its components during two seasons. The results revealed that Sakha1 variety produced the highest values of seeds, straw, and biological yields in both seasons, Espain cultivar recorded the highest value of 100-seed weight and number of seeds /pod during the two growing seasons, while the highest value of the plant height and number of pods per branche were obtained by Mariout 2 broad bean variety in both seasons. The results indicated that increasing phosphorus fertilizer addition up to 50 P2O5 kg\fed, recorded significant increments in all traits of faba bean under this study except phosphorus use efficiency which was significantly decreased with increasing phosphorus rates. Fertilizing faba bean cultivar Sakha-1 with 50 kg P2O5/fed gave the highest values of yield and its component under environmental conditions of El-Kharga location. The highest positive correlation and coefficient were observed between seed yields and most of the studied traits as an average of four cultivars and three levels of phosphorus in both seasons. As a result, the PC1, PC2and PC3 were retained for the final analysis, in which the three PCs expressed more variability and support the choice of the trait with a positive loading factor. Principle component analysis showed that out of all PCs, the three first main PCs (PC1, PC2 and PC3) extracted had eigenvalues larger than one, while the rest of the other PCs had eigenvalues less than one (Eigenvalue <1).