This survey explores advancements in automated species detection and classification, emphasizing the integration of deep learning (DL) and Internet of Things (IoT) technologies for ecological monitoring and biodiversity conservation. State-of-the-art DL architectures, including YOLO, ResNet50, and CNN-based cascade filtering, demonstrate significant potential in addressing challenges such as environmental variability, species diversity, and real-time monitoring. IoT-enabled frameworks, leveraging hardware like Arduino and NodeMCU, facilitate scalable, field-deployable solutions through real-time data collection and wireless communication. Additionally, sound-based classification systems, incorporating audio feature extraction and machine learning models, expand detection capabilities for avian species. By summarizing key methodologies, datasets, and challenges, this paper highlights opportunities for improving robustness, dataset diversity, and energy-efficient IoT deployments to advance biodiversity monitoring efforts.