Machine breakdowns cause massive losses in production and efficiency. Predicting them is challenging. Traditional non-AI-based solutions may cause some/all of the following problems: 1\. Unplanned downtime & Reduction in productivity 2\. Severe accidents & Reduced lifetime of machines 3\. Financial losses AI has the potential to solve the aforementioned problems. However, current solutions rely on IoT/cloud computing, and suffer from the following drawbacks: 1\. Power-hungry & Expensive 2\. Data privacy issues 3\. Increased carbon footprint To quantify, a Cloud AI camera consumes 325 kg CO2 /year. This is because of the large amount of data transmission required for the camera. This project aims to create embedded system solutions that use edge AI to predict and prevent machine breakdowns. The solution analyses collected sensor data to monitor the machines and reduce the risk of a breakdown. The solution is: 1\. Low-cost & Robust 2\. Environment-friendly 3\. Data secure (In-house) To quantify, an edge AI Camera consumes only 4 kg CO2 /year