This study introduces a novel adaptive reconstruction algorithm based on the time reversal filtering factor (TRFF) method, aimed at improving the quality and accuracy of magnetic thermoacoustic (MTA) imaging. The TRFF method overcomes the limitations of traditional time-reversal (TR) techniques by incorporating a dynamically adjustable filtering factor and a weighting function that adaptively processes acquired signals. This adaptive approach assigns higher weights to high-quality signals, while effectively suppressing noise and artifacts. We applied the TRFF algorithm to both numerical simulations and experimental setups, demonstrating its effectiveness in improving signal-to-noise ratio (SNR), reducing artifacts, and enhancing the contrast of target signals. In numerical simulations, we compared the TRFF method to conventional TR methods, using metrics such as root mean square error, peak SNR, and structural similarity index. The results highlighted the superior performance of the TRFF method in reconstructing high-quality images. For experimental validation, we utilized the TRFF algorithm for multi-layer processing of three-dimensional MTA data, significantly improving imaging quality for deep tissues. We optimized a 16-channel array ultrasonic transducer (AUT-16) for efficient three-dimensional imaging. Separately, a 128-channel arc-shaped AUT (AAUT-128) was developed to achieve real-time imaging. The AUT-16 enabled faster scanning and better spatial information reconstruction, while the AAUT-128 facilitated high-frame-rate real-time imaging of dynamic magnetic nanoparticles, showcasing its potential for dynamic biomedical monitoring. This study marks significant advancements in both signal processing and hardware design for MTA imaging. The integration of the TRFF method enhances both pseudo-3D and real-time imaging capabilities, presenting a promising approach for future applications in biomedical diagnostics and complex tissue imaging.