The proliferation of aggressive dynamic pricing strategies within contemporary e-commerce platforms has fundamentally altered consumer purchasing environments. These algorithmic pricing models, driven by real-time market shifts, inventory levels, and competitor actions, generate significant price volatility, leading to inherent information asymmetry. Consumers frequently lack access to crucial historical price trajectories and comprehensive cross-platform comparisons, resulting in suboptimal purchasing decisions, missed financial opportunities, and diminished overall market transparency. To mitigate this systemic challenge, this paper presents the design and implementation of a robust E-commerce Price Tracker System. The proposed architecture utilizes a Python-based backend to perform scheduled, real-time data aggregation through integration with external e-commerce APIs, such as Keepa for Amazon and dedicated interfaces for other major retailers. All fetched price points and associated metadata are meticulously timestamped and persistently archived within a scalable NoSQL repository (Firebase Real-time Database or Firestore), thereby establishing a reliable time-series dataset suitable for rigorous longitudinal analysis.