Time-series forecasting is crucial for data-driven decisions across finance, healthcare, and environmental monitoring. Despite technological advances, identifying significant temporal segments impacting predictions remains challenging. We introduce ConformaSegment, a model-agnostic explainability framework that enhances time-series interpretability by identifying critical segments while quantifying prediction uncertainty. The framework integrates conformal prediction to generate reliable prediction intervals with guaranteed coverage rates, enabling users to understand which temporal segments most significantly influence forecasting outcomes. Our approach was validated across diverse real-world datasets using LSTM, RNN, and GRU models, demonstrating substantial performance improvements over existing techniques such as Saliency Maps and Integrated Gradients. ConformaSegment achieved mean R \(^2\) improvements of 42% and 18% respectively over these methods, while enhancing prediction interval coverage by 25.73% and 40.15%. These results demonstrate that ConformaSegment effectively identifies critical time segments in forecasting tasks, improving both interpretability and uncertainty quantification, thus enhancing model trustworthiness for applications in healthcare, industrial maintenance, and other time-sensitive domains.