Automated manufacturing, driven by rising demands for mass-produced products, calls for efficient systems such as the peg-in-hole assembly. Traditional industrial robots perform these tasks but often fall short in speed during pick-and-place processes. This study presents an innovative mechatronic system for peg-in-hole assembly, integrating a novel peg insertion tool, assembly mechanism and control algorithm. This combination achieves peg insertion with a 200 µm tolerance without the need for pick-and-place, meeting the requirements for high precision and rapidity in modern manufacturing. Dual cameras and computer vision techniques, both traditional and machine learning (ML)-based, are employed to detect workpiece features essential for assembly. Traditional methods focus on image enhancement, edge detection and circular feature recognition, whereas ML verifies workpiece positions. This research also introduces a novel statistical quality inspection, offering an alternative to standard ML inspections. Through rigorous testing on varied workpiece surfaces, the robustness of the methods is affirmed. The assembly system demonstrates a 99.00% success rate, while the quality inspection method attains a 97.02% accuracy across diverse conditions, underscoring the potential of these techniques in automated assembly, defect detection and product quality assurance.