Skin　cancer　diagnosis　is　difficult　due　to　lesion　presentation　variability.　Conventional　methods　struggle　to　manually　extract　features　and　capture　lesions　spatial　and　temporal　variations.　This　study　introduces　a　deep　learning-based　Convolutional　and　Recurrent　Neural　Network　(CNN-RNN)　model　with　a　ResNet-50　architecture　which　used　as　the　feature　extractor　to　enhance　skin　cancer　classification.　Leveraging　synergistic　spatial　feature　extraction　and　temporal　sequence　learning,　the　model　demonstrates　robust　performance　on　a　dataset　of　9000　skin　lesion　photos　from　nine　cancer　types.　Using　pre-trained　ResNet-50　for　spatial　data　extraction　and　Long　Short-Term　Memory　(LSTM)　for　temporal　dependencies,　the　model　achieves　a　high　average　recognition　accuracy,　surpassing　previous　methods.　The　comprehensive　evaluation,　including　accuracy,　precision,　recall,　and　F1-score,　underscores　the　model＇s　competence　in　categorizing　skin　cancer　types.　This　research　contributes　a　sophisticated　model　and　valuable　guidance　for　deep　learning-based　diagnostics,　also　this　model　excels　in　overcoming　spatial　and　temporal　complexities,　offering　a　sophisticated　solution　for　dermatological　diagnostics　research.