Recycling is imperative to improving the carbon footprint left by humans as the mass production and disposal of items is not only wasteful but harmful to the environment. Generally to separate one's trash from their recyclables they have to dispose of it in the proper recycle and or compost bin. Unfortunately, humans are lazy, not wanting to take the extra time to complete the mundane task of separating their trash. This becomes a problem as recycling plants pick up these non-recyclable items that tend to jam up their recycling plant, and must be removed manually; reducing the effectiveness of the recycling plant. To tackle this issue on the front end, we propose using an automatic trash sorting system that will place a piece of trash in its appropriate trash or recycle bin. This sorting system will use an AI model that is trained using a database of tagged images of trash and recyclable items to determine whether a piece of trash can be recycled or not. We learned AI technologies such as ResNet and Tensorflow, researching these items extensively to figure out how they work. We learned about skip connections, gradient descent, and other elements relating ResNet and we were able to use this knowledge to create a working ML model that sorts recyclables and trash. We faced many challenges in streamlining the process to create a model with the highest accuracy but were eventually able to create a spreadsheet to track this and divide up the work. Additionally, we had trouble figuring out how to get a data set, but we were eventually able to find one online and train our model.