Visual impairment increases fall risk, particularly among older adults with low vision facing a 16% higher likelihood of falls and those with blindness experiencing a 40% increased risk. To address this, the research presents an ML-driven, IoT-enabled smart cane equipped with sensor-based behavior analysis for real-time fall detection and mobility assistance. The system analyzes motion patterns and sudden orientation changes that helps in detecting falls while integrating obstacle detection with multi-modal feedback. Designed with low-power and cost-effective embedded components, the system ensures efficiency on resource-constrained devices, while IoT connectivity enables remote monitoring and caregiver communication. This smart cane offers a cost-effective, scalable solution to improve independence and quality of life for visually impaired individuals.