To enhance bug detection, we propose developing an AI-powered code editor that using deep learning to transform software development. Trained on large code repositories, allows for the real-time detection of bugs, simplifying the development process and lowering the need for time-consuming manual bug detection. The focus extends beyond conventional bug identification, aiming to expedite the bug-fixing process and empower developers for more creative problem-solving. Our objectives include creating a robust AI-assisted code editor that continuously learns from developer interactions, ensuring adaptability to evolving coding standards and emerging bug trends. To find code mistakes, the study uses deep learning models, namely Gated Recurrent Unit (GRU) networks, Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNNs). First, the source code is used to create Abstract Syntax Trees (ASTs), which are then encoded recursively to capture the syntactic structure and semantics. The neural networks are then trained and assessed using these encoded representations. This research addresses challenges like variable-length code handling and dataset quality. It also explores the intricate aspects of code context and semantics, distinguishing between abstract syntax trees and semantic relationships. Extensive tests employ measurements like F1 score, accuracy, precision, recall, to examine the efficacy of various neural network topologies. The GRU model performs better than the others, according to the results, proving its greater capacity to identify flaws. Anticipated outcomes include a transformative impact on software quality, reduced development timelines, and increased overall efficiency in the software development lifecycle, marking a significant advancement in bug detection methodologies.