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Wild Animal Detection And Segmentation Play A Vital Role In Wildlife Monitoring, Biodiversity Conservation, And Prevention Of Human–animal Conflicts. Traditional Methods Such As Manual Observation And Camera Traps Are Often Time-consuming, Labor-intensive, And Prone To Inaccuracies In Large-scale Or Dense Environments. With Recent Advancements In Deep Learning, Automated Detection Systems Have Become Increasingly Effective For Real-time Monitoring Of Wildlife. This Study Proposes An Efficient Framework For Wild Animal Detection And Segmentation Using The YOLOv7 (You Only Look Once Version 7) Object Detection Algorithm. YOLOv7 Is Utilized For Its Superior Speed And Accuracy In Identifying Multiple Animal Species Within Complex Natural Backgrounds. Furthermore, Segmentation Is Integrated To Provide Precise Boundary Information, Enabling Detailed Analysis Of Animal Size, Movement Patterns, And Habitat Utilization. The Proposed System Can Assist Researchers, Forest Authorities, And Conservationists In Automating Wildlife Surveillance, Reducing Manual Effort, And Enhancing Decision-making For Ecological Management.