Red seaweeds such as Kappaphycus alvarezii and Porphyra yezoensis have many applications, especially in the food industry, which suggests the need for knowing their potential allergenicity. In silico approaches can be used to determine if a protein is an existing allergen or has the ability to cross-react with one. In this study, 318 sequences for Kappaphycus alvarezii and 641 protein sequences for Porphyra yezoensis were screened for potential allergens using AlgPred 2.0 and AllergenOnline, followed by the FAO allergenicity test using Allermatch. Data from this were used to predict the B-cell epitopes using the IEDP prediction tool and T-cell epitopes using MHC2Pred and were modeled using SWISS-MODEL and PyMOL to highlight specific epitopes. These models were assessed for quality using Global Quality Model Estimate (GQME) scores, ERRAT scores, and VERIFY 3D. Results showed fourteen (14) potential red seaweed allergens, four (4) of which were found in Kappaphycus alvarezii and ten (10) in Porphyra yezoensis. Several proteins of red seaweeds shared structural similarities with species normally associated with food allergies, such as common hazel, Atlantic salmon, and shark catfish, as well as other types of allergens such as those in house-dust mites, that could potentially induce cross-reactivity. Additionally anticipated were specific B-cell and T-cell epitopes and their specific peptide sequences that were incorporated in the 3D models, which were created for further comparison with other molecular structures of recognized allergens. Almost all of the 3D models had a GQME score of above 0.7 and had a high ERRAT score for overall quality but some failed to pass the VERIFY 3D test. This study could serve as a preliminary yet robust approach to identifying allergenic proteins in red seaweed and narrowing down potential existing cross-reactive allergens from various species that could aid in future in vitro and in vivo allergenicity studies.