Dental　health　plays　a　pivotal　role　in　determining　the　general　health　and　quality　of　life　of　a　person.　Dental　caries　is　a　prevalent　health　problem　in　the　world;　therefore,　prompt　and　effective　dental　caries　treatment　is　necessary　for　individuals　who　require　pain　relief.　Conventional　methods　of　diagnosing　dental　diseases,　like　visual　examination　and　radiographic　testing,　depend　on　qualified　medical　professionals　and　can　be　incredibly　labor-intensive　and　imprecise　in　their　diagnosis.　To　overcome　these　challenges,　this　study　designed　a　deep　learning　(DL)　model　for　the　diagnosis　of　several　dental　conditions　such　as　tooth　decay,　non-periodontal,　and　periodontal　disease.　A　novel　model　named　periodontal　disease　classification　network　(PDCNET)　based　on　convolutional　neural　network　(CNN)　has　been　developed　for　the　identification　of　periodontal　disease　using　dental　radiographs.　Additionally,　the　proposed　PDCNET　model　has　been　evaluated　on　two　publicly　available　benchmark　datasets　of　dental　caries.　To　handle　the　imbalanced　classes　of　the　dental　caries　dataset,　this　study　used　the　SMOTE　TOMEK　method　to　generate　new　synthetic　samples　for　the　minority　categories　to　ensure　the　periodontal　disease　dataset　is　balanced.　The　proposed　PDCNET　model　acquired　a　99.79%　AUC,　98.39%　recall,　98.39%　accuracy,　98.39%　precision,　and　an　F1-score　of　98.31%.　Furthermore,　the　performance　of　the　proposed　PDCNET　model　is　contrasted　using　the　six　baseline　pre-trained　classifiers　such　as　EfficientNet-B0　(M1),　DenseNet-201　(M2),　Vgg-16　(M3),　Vgg-19　(M4),　Inception-V3　(M5),　and　MobileNet　(M6)　in　terms　of　many　parameters.　The　levels　of　accuracy　attained　by　M1,　M2,　M3,　M4,　M5,　and　M6　are　85.94%,　94.37%,　91.96%,　93.57%,　89.95%,　and　94.77%,　respectively.　The　findings　showed　that　the　PDCNET　model　has　superior　outcomes　as　compared　to　baseline　models　and　provides　significant　assistance　to　the　dentist　in　the　diagnosis　of　dental　disease.