Geosteering, the art of navigating wells to maximize the reservoir resources, is fraught with challenges of geological uncertainty and the relentless pace of real-time operations. In this paper, we present a novel framework that integrates Particle Filters (PF) for probabilistic subsurface interpretation with a Dual-Network Deep Reinforcement Learning (DRL) model for adaptive decision-making in geosteering operations. The PF component quantifies subsurface uncertainties, providing a probabilistic interpretation of geological boundaries, while the DRL model leverages this information to generate optimal steering decisions. This synergy ensures robust trajectory planning that dynamically adapts to real-time geological changes. The framework incorporates key features, such as target-line alignment to maintain wellbore proximity to reservoir zones and dog-leg severity constraints to ensure operational feasibility. Extensive verification in an industry-standard environment accessed via an API demonstrates the model’s ability to accurately track reservoir boundaries, predict gamma-ray values, and optimize well trajectories. The results highlight significant improvements over traditional geosteering approaches and standard DRL-based methods in terms of reservoir contact, decision-making efficiency, and trajectory accuracy, even in low-data scenarios. The proposed framework provides a scalable and robust solution for quantifying uncertainties in real-time geosteering, paving the way for informed operational decisions improving value-creation and drilling effciency.