Mosquito habitat ranges are projected to expand due to climate change. This investigation aims to identify future mosquito habitats by analyzing the preferred ecological conditions of mosquito larvae. After assembling a data set with atmospheric records and citizen-science larvae observations, a deep neural network is trained to predict larvae counts from ecological inputs. Time series forecasting is conducted on these variables and climate projections are passed into the initial deep learning model to generate location-specific mosquito larvae abundance predictions. The results support the notion of regional ecosystem-driven changes in mosquito spread, with high-elevation regions increasing in susceptibility to mosquito infestation.