Skin-related disorders often go unnoticed until they progress to severe stages, primarily due to limited awareness and late-staged diagnosis. Existing diagnostic systems primarily rely on image analysis, neglecting symptoms and environmental triggers. This paper proposes an integrated deep learning framework that combines Convolutional Neural Networks (CNN) with symptom and environmental data for accurate and context- aware dermatological diagnosis. The system uses EfficientNet for image-based disease detection (e.g., psoriasis, vitiligo, rosacea), integrates user-reported symptoms (irritation, redness, flaking, dryness), and fetches real-time weather data (temperature, humidity, UV index) via API. A multimodal fusion mechanism is employed to improve diagnostic confidence and severity assessment. Personalized skincare recommendations are generated based on environmental conditions. Experimental validation on a curated dataset shows an estimated accuracy of 94.2% with a precision of 93.8% and recall of 94.5%. The system bridges the gap between automated diagnosis and environmental awareness, offering a proactive skin health management tool.