Magnetic Resonance Imaging (MRI) reconstruction remains a vital research domain, with ongoing efforts directed at enhancing image quality while minimizing acquisition time. Conventional k-space filling techniques have traditionally relied on Fourier transform properties and interpolation methods. However, recent innovations have introduced deep learning and compressed sensing as optimization strategies for this process. This study introduces a novel super-resolution framework that incorporates Toeplitz matrices for structured k-space completion, integrated with deep learning-based models and compressed sensing methodologies. Given their intrinsic connection to convolutional operations, Toeplitz matrices provide a mathematically sound foundation for defining k-space structures while ensuring data consistency. Within this framework, deep neural networks are employed to infer the underlying k-space distribution in PROPELLER sequences from sparsely sampled data, while Toeplitz matrix constraints are utilized to maintain coherence. Additionally, the application of compressed sensing principles—incorporating sparsity priors and regularization techniques—improves both robustness and image quality, facilitating high-fidelity reconstructions from substantially undersampled acquisitions. The proposed approach is validated using both simulated and real MRI datasets, demonstrating that it effectively reduces reconstruction error and enhances image quality in comparison to traditional interpolation methods and standalone deep learning models. The findings indicate that combining sequentially rotating blade raw data acquisition with structured priors based on Toeplitz matrices, deep learning-driven inference, and compressed sensing optimization can yield more precise and computationally efficient MRI reconstructions, ultimately contributing to faster scan times in clinical applications.