This study proposes an adaptive filter based on a manifold absolute pressure (MAP) sensor in order to control automotive engines. The proposed adaptive filter, which is based on the least mean squares (LMS) algorithm, is intended to reduce the impacts of sensor noise and nonlinearity, which can result in false readings and a subsequent decline in engine performance. The filter can be used for long-term engine control applications because it is implemented on a model-based system and can adapt to changes in the sensor's properties over time. The suggested filter efficiently decreases sensor noise and increases the accuracy of MAP sensor readings, according to experimental data, which also indicate a roughly 10% rise in mean absolute percentage error (MAPE) compared to the standard lowpass filter. The filter's versatility also enables reliable operation under a variety of operating conditions and sensor characteristics. Additionally, the filter's signal-to-noise ratio (SNR) enhancement is almost 10% greater than that of a traditional lowpass filter, resulting in enhanced engine performance and fuel economy. Overall, the suggested adaptive filter appears to be a viable option for improving the performance of MAP sensors in automotive engine control applications.