This paper proposes an Optimized Adaptive Reconfigurable (OAR) FIR Filter Architecture for speech signal processing, addressing the need for low-power, area-efficient, and high-performance adaptive filtering. The design supports two dynamically switchable modes: a conventional LMS-based filter for precise adaptation and a multiplierless Distributed Arithmetic (DA) mode for resource efficiency. Mode selection is driven by a real-time decision mechanism based on input amplitude and filter coefficient thresholds. Functional validation was performed through simulation, while hardware performance metrics like power and area were evaluated to be reduced by 30%