Medical imaging technologies are crucial for precise disease diagnosis, encompassing X-rays, Magnetic Resonance Imaging (MRI), and Ultrasound scans. However, these methods have limitations such as ionizing radiation, cost, time consumption, or compromised image quality. Photoacoustic Tomography (PAT) is a promising noninvasive technique providing high-resolution, high-contrast images in deep tissues. In this paper we integrate the plug-and-play framework to enhance compressive sensing algorithms for PAT imaging with reduced samples as it offers faster image reconstruction, optimized memory, and expedited data transmission in real-world PAT applications. Our approach formulates and evaluates novel Split Bregman Total Variation (SBTV) and Relaxed Basis Pursuit ADMM (rBP-ADMM) algorithms fortified with Plug-and-Play (PnP) denoising techniques. These algorithms are assessed under diminishing sample conditions with various PnP denoisers. Notably, SBTV coupled with Bilateral Filtering as a PnP denoiser yields significant SSIM enhancements (11-17%). These findings highlight the potential of PnP-enhanced SBTV for robust image reconstruction in low-sample scenarios, addressing a critical need for efficient PAT applications.