Nowadays, the presence of the latest technologies like Artificial Intelligence and lenses that can capture the micro-living being like larva have been used in our surrounding environment. Deep Learning technologies which are a subset of Artificial Intelligence have been implemented in used for processing the image. As before this, there is a study to detect the possible place of Aedes mosquito breeding place with the use of Internet of Things (IoT) technologies to detect the humidity of certain places and relate it to the possibility of Aedes mosquito breeding present. To support the study and have verification of the place is the breeding place of Aedes mosquito, a study to classify the larva and detect it has been proposed. The Aedes Larvae Classification and Detection (ALCD) System by using Deep learning is a system that uses deep learning technologies to detect the pattern of the larva and classify it according to its type. The proposed developed system ALCD because Malaysia is having a rapid increase in dengue cases throughout the year. While there are many approaches from the government and non-government organizations (NGOs) to help combat the dengue virus outbreak, this study is focusing on preventing the virus from spreading in the early stages. The life cycle of an Aedes mosquito is starting from the egg to larva to pupa and lastly became an adult mosquito. The early stages of Aedes mosquito that can be used to differentiate between Aedes and Non-Aedes were at the larva stages. This study is meant to do a background study on using the latest technology of deep learning subset of Artificial Intelligence technology to find the pattern of the Aedes and Non-Aedes on the larva. After the pattern of the larva type is recognized, the process to classify it between the Aedes larvae and Non-Aedes larvae can be continued for classification. Real-time classification testing will be conducted to test the accuracy and efficiency of the ALCD system. © 2020 IEEE.