India's electric two-wheeler market is growing at a pace that exposes a critical and under addressed infrastructure gap: Lithium Iron Phosphate (LFP) battery packs consistently fall short of their rated lifecycle under the country's harsh operating conditions, degrading within four to six years against a theoretical seven to ten year design life. The primary drivers of this gap—thermal stress from Indian summer temperatures, deep-discharge habits, and aggressive urban riding patterns—are well-documented in battery degradation literature yet remain unaddressed by the static, rule-based Battery Management Systems (BMS) prevalent in the Indian market today. This paper proposes a conceptual architecture for an AI-driven predictive BMS that integrates three intelligence layers across the edge, connectivity, and cloud domains. At the edge, real-time battery state estimation and anomaly detection are performed using established signal processing and embedded machine learning techniques. A low-power cellular IoT link transports telemetry securely to a cloud pipeline where a layered machine learning stack handles degradation modeling, lifecycle prediction, and behavioral risk scoring. The proposed framework theoretically projects a battery service life extension from approximately five years to seven years by combining three independently documented degradation suppression mechanisms: State-of-Charge window enforcement, thermal stress reduction, and discharge behavior modification. Each mechanism is grounded in peer-reviewed battery degradation studies. This paper intentionally presents the system at the architectural concept level, with technical implementation details available through the referenced literature and through direct correspondence with the author.