Seamless handoff remains a critical challenge in IEEE 802.11 wireless networks, particularly in dense and highly mobile environments where conventional reactive handoff mechanisms based primarily on Received Signal Strength (RSS) may trigger decisions only after significant degradation in link quality. Recent studies have demonstrated the potential of Machine Learning (ML), edge computing, and Software-Defined Networking (SDN) to improve handoff prediction, reduce unnecessary handovers, and enhance throughput. However, existing approaches largely emphasize handoff latency, success rate, Signal Strength (SS), or general throughput optimization, while the prediction of impending handoff based on the expected Data Rate (DR) of the serving and neighbouring Access Points (APs) remains insufficiently explored. This limitation is significant because a reduction in RSS does not necessarily imply an immediate deterioration in achievable DR, and an AP with stronger SS may provide lower effective DR because of congestion, interference, channel conditions, or resource utilization. This study therefore presents a predictive inter-cell handoff technique for IEEE 802.11 wireless networks that incorporates data-rate trends into the handoff decision process. The method continuously monitors historical and real-time network parameters, with particular emphasis on the user's current and predicted DR , to anticipate service degradation before it becomes critical. By predicting the future data-rate condition of the serving cell and comparing it with candidate neighbouring cells, the technique aims to identify an appropriate handoff opportunity proactively rather than waiting for conventional threshold-based degradation. The approach reduces unnecessary handoffs, minimize service interruption, and maintain a more stable and higher-quality DR during user mobility. Consequently, the study addresses a key gap in existing IEEE 802.11 handoff mechanisms by shifting the decision criterion from predominantly signal-strength-based handoff toward data-rate-aware predictive handoff, providing a basis for more reliable mobility management and improved Quality of Service (QoS) in dynamic wireless environments.