Ecological biodiversity is declining at an unprecedented rate. To combat such irreversible changes in natural ecosystems, biodiversity conservation initiatives are being conducted globally. However, the lack of a feasible methodology to quantify biodiversity in real-time and investigate population dynamics in spatiotemporal scales prevents the use of ecological data in environmental planning. Traditionally, ecological studies rely on the census of an animal population by the “capture, mark and recapture” technique. In this technique, human field workers manually count, tag, and observe tagged individuals, making it time-consuming, expensive, and cumbersome to patrol the entire area. Recent research has also demonstrated the potential for inexpensive and accessible sensors for ecological data monitoring. However, stationary sensors collect localised data, which is highly specific on the placement of the setup. Existing approaches to monitor ecological biodiversity follow a localised approach, leading to uncertain results due to extrapolation of results while scaling to a large geographical region. This report summarizes the existing data collection techniques and the application of deep learning algorithms on such collated datasets to identify the inherent challenges in the existing approaches, which can be addressed further. The characteristic advantages, limitations, and prior work have been extensively studied to survey various techniques utilized for biodiversity monitoring.