Offshore geological exploration increasingly confronts operational and environmental challenges, including ecological constraints, elevated costs, and limited availability of high-resolution geospatial data. This study proposes a quantum-enhanced simulation framework that integrates Quadratic Unconstrained Binary Optimization (QUBO) with the Quantum Approximate Optimization Algorithm (QAOA) to optimize route planning for early-stage geological reconnaissance in the Guajira Offshore basin, Colombia. The study domain—a 1 km² marine area—is discretized into a 10×10 grid of 100 m × 100 m cells, each assigned a geostructural priority score. The resulting QUBO formulation encodes spatial connectivity, movement rules, and geological relevance into a binary decision structure, which is then solved through hybrid quantum–classical optimization routines. Results indicate that QAOA-based solutions produce coherent and efficient exploration routes that prioritize high-value geological targets while respecting spatial continuity and energy limitations. The proposed framework offers a promising pathway toward low-impact, cost-effective offshore exploration strategies and is scalable to broader maritime contexts. Additionally, it is compatible with future extensions involving AI-driven geological classification and real-time, adaptive route planning using multi-agent systems.