Traffic sign recognition has been used as an input for managing speed of electric vehicles that are connected with their systems. This paper discusses how to integrate a visual recognition system with embedded propulsion control and IoT monitoring. Fifteen full-text studies published from 2021 to 2025 were reviewed. Literature contains deep learning techniques to detect and classify traffic signs; Indian roads dataset; IoT speed monitoring architecture; prototype-level speed control system. Recent detection studies show that end-to-end deep models can identify signs under complex conditions; for example, a refined Mask R-CNN study on 87 Indian sign categories reported 97.08% precision, while a YOLOv5 comparison reported 97.70% mAP@0.5 on its dataset. Control literature shows that pulse-width modulation (PWM) motor control, sensor feedback, and cloud logging can convert a known speed limit to an observable and traceable vehicle response. A low-cost prototype architecture is synthesized from the reviewed evidence: an ESP32-CAM captures signals, an ESP32 coordinates decisions and connectivity, an L298N applies pulse-width modulation to a DC traction motor, and a display/cloud service provides driver and supervisor feedback. The review finds that the limiting issue is no longer recognition accuracy alone: safe deployment requires confidence-aware decisions, low end-to-end latency, fail-safe override, locally representative data, and cybersecurity-aware IoT design. This model can serve as an academic and practical basis to develop an IoT-based electric vehicle speed control system.

Keywords— traffic sign recognition; electric vehicles; Internet of Things; automatic speed control; convolutional neural networks; intelligent transportation systems.