Optofluidics as a relatively new field of biomedical detections has become an attractivetechnology that combines integrated photonics and microfluidics. This brings together the best of optical sensing and microfluidic media for biological fluidic compatibility to attain high sensitivity. Integrated designs with compact and portable footprints are a unique feature of optofluidics which makes it a great candidate for point-of-care (POC) applications. Even though there have been outstanding results in terms of sensitivity and multiplexing capability reported in the past couple of decades, there is still significant room for innovation to boost performance. The dynamic environment of a microfluidic channel causes ultrasensitive photonic sensing challenges because of many variables changing from chip to chip, sample to sample, and even in the course of a measurement. First, we explore the potential of incorporating microfluidic techniques, specifically, 3D hydrodynamic focusing (3DHDF) to minimize these variations within the experiment. With the help of 3DHDF, we are able to focus the biological sample fluid into a narrow stream where all of the flowing target particles experience similar light-matter interaction. This, of course, requires a change in the chip design and involves a more complicated fabrication process. Therefore, other alternative approaches were considered. A robust powerful event detection framework is introduced. It suits the multiplexed real-time pathogen detection in the presence of possible background and noise signals as well as signal variations. The use of continuous wavelet transform (CWT) in this algorithm alongside the custom wavelet design takes advantage of multiscale analysis to detect events across scales. Next, in a different approach, a machine learning framework with a convolutional neural network (CNN) is used for classifying PCWA-detected events very efficiently and fast. The framework is further pushed in terms of compatibility with current trends toward edge devices by a successful implementation of the full real time event detection and classification on an edge device is demonstrated. This proves the capacity of edge computing devices for a broad range of applications such as portable ultrasensitive biomedical diagnosis instruments. Furthermore, the current multiplexing concept is expanded towards the high concentration range of operation. In this regime (also called analog regime), individual events are not detectable anymore and the measurement relies on the analog signals recorded from highly concentrated solutions. Wavelength division multiplexing (WDM) was previously used successfully for multiplexed detection of individual events by encoding spectral information into the time domain. Here, a hybrid adaptive scheme adds time division multiplexing into this recipe to increase the dynamic range of the optofluidic biosensor. The results show significant (four orders of magnitude) expansion of the multiplexed detection at higher concentrations while preserving the already established performance at lower concentrations.