Despite substantial global investments in ecological engineering (EE), its long-term effectiveness in karst regions remains poorly quantified, particularly regarding how landscape patterns influence habitat quality (HQ) under EE interventions. This study therefore aims to systematically evaluate the spatiotemporal evolution, coupling characteristics, and driving mechanisms of HQ following EE implementation in karst regions. We integrate multisource remote sensing data, the InVEST model, quadratic regression, entropy summation, and random forest (RF) methods. Our findings show that human activities exacerbate landscape fragmentation, causing a 1.37% decline in regional HQ. Among three EE types-natural conservation (NC), human intervention (HI), and terrain optimization (TO)-NC achieves the fastest benefit peak (5.89 years) and best performance in enhancing HQ (annual increase of 0.126) while suppressing degradation (annual decrease of degradation degree by 0.002). TO shows long-term improvement potential (peak at 18.26 years), whereas HI improves HQ but fails to control degradation simultaneously. Landscape pattern analysis identifies the Shannon diversity index (SHDI) as the most environmentally sensitive factor (entropy weight 0.202), and the aggregation index (AI, not to be confused with artificial intelligence) as the dominant factor shaping HQ spatial patterns (RF importance = 0.187). Elevation (rho = 0.54) and precipitation (rho = 0.43) significantly promote HQ, while temperature (rho = -0.28) inhibits ecological recovery. Based on these results, we propose an analytical framework that evaluates ecological restoration effectiveness across three dimensions: net engineering benefits, temporal dynamics of benefits, and threshold responses of landscape patterns. This framework supports precision ecological restoration decision-making in karst and similar fragile ecosystems.