This paper presents preliminary insights into the detection of invasive snail species using an artificial intelligence (AI)-powered canine forensics framework, driven by real-world field data. Leveraging trained detection dogs and sensor-integrated AI frameworks, the study explores the intersection of biological nature and algorithmic intelligence. The principal contributions are as follows: (1) development of an AI-assisted canine detection framework that combines scent detection with computational analysis; (2) identification of key volatile organic compounds (VOCs) for a specific species of interest (based on relevance as a pest) to establish digital scent markers; (3) implementation of a support vector machine (SVM) classifier achieving >90% accuracy, with macro precision and recall each over 90% as well—highlighting the reliability of VOC profiles as scent-based digital analogs; and (4) recognition of the need for larger datasets and real-time field deployment to further validate and enhance this methodology. This early-stage research lays the groundwork for scaling up AI-assisted canine forensics and underscores its potential in species-specific monitoring and environmental threat mitigation.