The demand for precise brain tumor detection and analysis in cancer diagnosis and treatment has escalated, necessitating extensive and diverse medical datasets. However, the acquisition of labeled tumor images is impeded by intricate dissimilarities between tumor and non-tumor regions, compounded by the diverse spectrum of brain tumor types. This study addresses a critical research gap by proposing a novel approach for robust and trustworthy brain tumor MRI image synthesis. Leveraging label conditional diffusion models, our approach adeptly captures specific tumor features, resulting in the generation of high-quality tumor images. Additionally, a trustworthiness control mechanism, employing evaluation metrics such as Fréchet Inception Distance (FID) and Inception Score (IS), ensures generated MRI brain tumor images meet exacting quality, accuracy, and clinical relevance criteria. Despite potential limitations in sharpness due to resolution constraints, our framework excels in overcoming challenges posed by image similarities and variations in brain tumor images. This surpasses the performance of conditional generative adversarial networks (GANs) by producing realistic details and textures. This contribution significantly advances the field of brain tumor image synthesis.