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Snakebite envenomation is a major public health concern, particularly in rural and underdeveloped regions where access to timely medical care is limited. Rapid and accurate identification of whether a snakebite is venomous or non-venomous is crucial for administering appropriate treatment and reducing mortality rates. This project proposes an intelligent Snake Bite Recognition System that leverages image processing techniques and Deep Neural Network (DNN) algorithms to automate the classification of bite marks.The system is developed using Python and is trained on a dataset comprising over 100 snakebite images, categorized into venomous and non-venomous classes. The dataset is divided into training and testing subsets to ensure model reliability and performance evaluation. Prior to classification, input images undergoseveral preprocessing steps including resizing, normalization, noise filtering, and feature extraction. These steps enhance image quality and enable the model to effectively learn distinctive features such as fang marks, puncture depth, and spatial patterns of the bite. A Deep Neural Network model is employed to analyze these extracted features and perform accurate classification. The system is designed to provide fast and reliable predictions, making it suitable for real-time applications. In addition to classification, the system offers preliminary medical guidance and first-aid recommendations, which can assist healthcare workers, first responders, and even non-experts during emergency situations until professional medical treatment is available.The proposed system is scalable and can be extended to identify other types of skin injuries such as insect bites, spider bites, or allergic reactions, thereby increasing its practical usability system.